Research on detection method of photovoltaic cell surface dirt based on image processing technology.

In view of the reduced power generation efficiency caused by ash or dirt on the surface of photovoltaic panels, and the problems of heavy workload and low efficiency faced by manual detection, this study proposes a method to detect dust or dust on the surface of photovoltaic cells with the help of image processing technology to timely eliminate hidden dangers and improve power generation efficiency.This paper introduces image processing methods based on mathematical morphology, such as image enhancement, image sharpening, image filtering and image closing operation, which makes the image better highlight the target to be recognized. At the same time, it also solves the problem of uneven image binarization caused by uneven illumination in the process of image acquisition. By using the image histogram equalization, the gray level concentration area of the original image is opened or the gray level is evenly distributed, so that the dynamic range of the pixel gray level is increased, so that the image contrast or contrast is increased, the image details are clear, to achieve the purpose of enhancement. When identifying the target area, the method of calculating the proportion of the dirt area to the whole image area is adopted, and the ratio exceeding a certain threshold is judged as a fault. In addition, the improved A* path planning algorithm is adopted in this study, which greatly improves the efficiency of the unmanned aerial vehicle detection of photovoltaic cell dirt, saves time and resources, reduces operation and maintenance costs, and improves the operation and maintenance level of photovoltaic units.

Over a decade of UAV incidents: A human factors analysis of causal factors.

This analysis examined systemic causes of Uncrewed Air Vehicle (UAV) accidents identifying operator, environmental, supervisory, and organisational factors through the use of the Human Factors Analysis and Classification System (HFACS). HFACS is a system-based analysis method for investigating the causal factors associated with accidents and incidents and has previously been used to reliably and systematically identify active and latent failures associated with both military and general aviation accidents. Whilst HFACS has previously been applied to UAV accidents, the last known application was conducted in 2014. Using reports retrieved from nine accident investigation organisations' databases, causal factors were coded against unsafe acts, preconditions, and failures at the supervisory, organisational, and environmental levels. Causal factors were assessed on 77 medium or large UAV mishaps/accidents that occurred over a 12-year period up to 2024. 42 mishap reports were deemed to involve a human factor as a causal factor. A large proportion of the mishaps contained factors attributed to Decision Errors at level 1 (Unsafe Acts) which was found to be associated with both the Technological Environment and Adverse Mental State at level 2 (Pre-conditions). Causal factors were identified at each of the other 3 levels (Supervisory, Organisational and External) with a number of emergent associations between causal factors. These data provide support for the identification and development of interventions aimed at improving the safety of organisations and advice of regulators for Uncrewed Air Systems.

Path Planning of Unmanned Aerial Vehicles Based on an Improved Bio-Inspired Tuna Swarm Optimization Algorithm.

The Sine-Levy tuna swarm optimization (SLTSO) algorithm is a novel method based on the sine strategy and Levy flight guidance. It is presented as a solution to the shortcomings of the tuna swarm optimization (TSO) algorithm, which include its tendency to reach local optima and limited capacity to search worldwide. This algorithm updates locations using the Levy flight technique and greedy approach and generates initial solutions using an elite reverse learning process. Additionally, it offers an individual location optimization method called golden sine, which enhances the algorithm's capacity to explore widely and steer clear of local optima. To plan UAV flight paths safely and effectively in complex obstacle environments, the SLTSO algorithm considers constraints such as geographic and airspace obstacles, along with performance metrics like flight environment, flight space, flight distance, angle, altitude, and threat levels. The effectiveness of the algorithm is verified by simulation and the creation of a path planning model. Experimental results show that the SLTSO algorithm displays faster convergence rates, better optimization precision, shorter and smoother paths, and concomitant reduction in energy usage. A drone can now map its route far more effectively thanks to these improvements. Consequently, the proposed SLTSO algorithm demonstrates both efficacy and superiority in UAV route planning applications.

Multi-UAV Cooperative Coverage Search for Various Regions Based on Differential Evolution Algorithm.

In recent years, remotely controlling an unmanned aerial vehicle (UAV) to perform coverage search missions has become increasingly popular due to the advantages of the UAV, such as small size, high maneuverability, and low cost. However, due to the distance limitations of the remote control and endurance of a UAV, a single UAV cannot effectively perform a search mission in various and complex regions. Thus, using a group of UAVs to deal with coverage search missions has become a research hotspot in the last decade. In this paper, a differential evolution (DE)-based multi-UAV cooperative coverage algorithm is proposed to deal with the coverage tasks in different regions. In the proposed algorithm, named DECSMU, the entire coverage process is divided into many coverage stages. Before each coverage stage, every UAV automatically plans its flight path based on DE. To obtain a promising flight trajectory for a UAV, a dynamic reward function is designed to evaluate the quality of the planned path in terms of the coverage rate and the energy consumption of the UAV. In each coverage stage, an information interaction between different UAVs is carried out through a communication network, and a distributed model predictive control is used to realize the collaborative coverage of multiple UAVs. The experimental results show that the strategy can achieve high coverage and a low energy consumption index under the constraints of collision avoidance. The favorable performance in DECSMU on different regions also demonstrate that it has outstanding stability and generality.

Energy-efficient quad tree-based clustering using edge-assisted UAV-relay to enhance network lifetime in WSN.

Wireless sensor networks' most prominent concern is energy optimization. It faces significant problems like high energy consumption, data loss, delay, and low network lifetime. To improve, it uses clustering. However, during clustering, coverage holes are most likely to appear near the network's edge, within the cluster, and between clusters. As a result, there are more energy holes and dead nodes; therefore, the goal of this work is to maximize node network lifetime and minimize energy consumption during data transmission in the wireless sensor network (WSN). The proposed work includes three entities: sensor nodes, an edge-assisted unmanned aerial vehicle (UAV), and a base station. It uses an edge-assisted unmanned aerial vehicle to provide additional resources to the UAV, which helps reduce energy consumption during data transmission. This research proposes using communication to enhance the speed and bandwidth of data transmission and reduce transmission latency. This work attempts to improve performance by increasing throughput.

Large field-of-view pine wilt disease tree detection based on improved YOLO v4 model with UAV images.

Introduction: Pine wilt disease spreads rapidly, leading to the death of a large number of pine trees. Exploring the corresponding prevention and control measures for different stages of pine wilt disease is of great significance for its prevention and control. Methods: To address the issue of rapid detection of pine wilt in a large field of view, we used a drone to collect multiple sets of diseased tree samples at different times of the year, which made the model trained by deep learning more generalizable. This research improved the YOLO v4(You Only Look Once version 4) network for detecting pine wilt disease, and the channel attention mechanism module was used to improve the learning ability of the neural network. Results: The ablation experiment found that adding the attention mechanism SENet module combined with the self-designed feature enhancement module based on the feature pyramid had the best improvement effect, and the mAP of the improved model was 79.91%. Discussion: Comparing the improved YOLO v4 model with SSD, Faster RCNN, YOLO v3, and YOLO v5, it was found that the mAP of the improved YOLO v4 model was significantly higher than the other four models, which provided an efficient solution for intelligent diagnosis of pine wood nematode disease. The improved YOLO v4 model enables precise location and identification of pine wilt trees under changing light conditions. Deployment of the model on a UAV enables large-scale detection of pine wilt disease and helps to solve the challenges of rapid detection and prevention of pine wilt disease.

YOLO SSPD: a small target cotton boll detection model during the boll-spitting period based on space-to-depth convolution.

Introduction: Cotton yield estimation is crucial in the agricultural process, where the accuracy of boll detection during the flocculation period significantly influences yield estimations in cotton fields. Unmanned Aerial Vehicles (UAVs) are frequently employed for plant detection and counting due to their cost-effectiveness and adaptability. Methods: Addressing the challenges of small target cotton bolls and low resolution of UAVs, this paper introduces a method based on the YOLO v8 framework for transfer learning, named YOLO small-scale pyramid depth-aware detection (SSPD). The method combines space-to-depth and non-strided convolution (SPD-Conv) and a small target detector head, and also integrates a simple, parameter-free attentional mechanism (SimAM) that significantly improves target boll detection accuracy. Results: The YOLO SSPD achieved a boll detection accuracy of 0.874 on UAV-scale imagery. It also recorded a coefficient of determination (R2) of 0.86, with a root mean square error (RMSE) of 12.38 and a relative root mean square error (RRMSE) of 11.19% for boll counts. Discussion: The findings indicate that YOLO SSPD can significantly improve the accuracy of cotton boll detection on UAV imagery, thereby supporting the cotton production process. This method offers a robust solution for high-precision cotton monitoring, enhancing the reliability of cotton yield estimates.

Prediction of wheat SPAD using integrated multispectral and support vector machines.

Rapidly obtaining the chlorophyll content of crop leaves is of great significance for timely diagnosis of crop health and effective field management. Multispectral imagery obtained from unmanned aerial vehicles (UAV) is being used to remotely sense the SPAD (Soil and Plant Analyzer Development) values of wheat crops. However, existing research has not yet fully considered the impact of different growth stages and crop populations on the accuracy of SPAD estimation. In this study, 300 materials from winter wheat natural populations in Xinjiang, collected between 2020 to 2022, were analyzed. UAV multispectral images were obtained in the experimental area, and vegetation indices were extracted to analyze the correlation between the selected vegetation indices and SPAD values. The input variables for the model were screened, and a support vector machine (SVM) model was constructed to estimate SPAD values during the heading, flowering, and filling stages under different water stresses. The aim was to provide a method for the rapid acquisition of winter wheat SPAD values. The results showed that the SPAD values under normal irrigation were higher than those under water restriction. Multiple vegetation indices were significantly correlated with SPAD values. In the prediction model construction of SPAD, the different models had high estimation accuracy under both normal irrigation and water limitation treatments, with correlation coefficients of predicted and measured values under normal irrigation in different environments the value of r from 0.59 to 0.81 and RMSE from 2.15 to 11.64, compared to RE from 0.10% to 1.00%; and under drought stress in different environments, correlation coefficients of predicted and measured values of r was 0.69-0.79, RMSE was 2.30-12.94, and RE was 0.10%-1.30%. This study demonstrated that the optimal combination of feature selection methods and machine learning algorithms can lead to a more accurate estimation of winter wheat SPAD values. In summary, the SVM model based on UAV multispectral images can rapidly and accurately estimate winter wheat SPAD value.

The specifics of the Galois field GF(257) and its use for digital signal processing.

An algorithm of digital logarithm calculation for the Galois field G F ( 257 ) is proposed. It is shown that this field is coupled with one of the most important existing standards that uses a digital representation of the signal through 256 levels. It is shown that for this case it is advisable to use the specifics of quasi-Mersenne prime numbers, representable in the form p = 2 n + 1 , which includes the number 257. For fields G F ( 2 n + 1 ) , an alternating encoding can be used, in which non-zero elements of the field are displayed through binary characters corresponding to the numbers + 1 and - 1. In such an encoding, multiplying a field element by 2 is reduced to a quasi-cyclic permutation of binary symbols (the permuted symbol changes sign). Proposed approach makes it possible to significantly simplify the design of computing devices for calculation of digital logarithm and multiplication of numbers modulo 257. A concrete scheme of a device for digital logarithm calculation in this field is presented. It is also shown that this circuit can be equipped with a universal adder modulo an arbitrary number, which makes it possible to implement any operations in the field under consideration. It is shown that proposed digital algorithm can also be used to reduce 256-valued logic operations to algebraic form. It is shown that the proposed approach is of significant interest for the development of UAV on-board computers operating as part of a group.

Risk assessment of environmental and bystander exposure from agricultural unmanned aerial vehicle sprayers in golden coconut plantations: Effects of droplet size and spray volume.

Unmanned aerial vehicle (UAV) sprayers are widely utilized in commercial aerial application of plant protection products (PPPs) in East Asian countries due to their high flexibility, high efficiency and low cost, but spray drift can lead to low utilization of UAV sprayers application, environmental pollution and bystander exposure risk. Droplet size and spray volume are critical factors affecting spray drift. Currently, the high temperature and humidity environment under the influence of the tropical monsoon climate brings new challenges for UAV sprayers. Therefore, in this study, pesticides were simulated with seduction red solution, and spraying trials were conducted using the DJI commercial T40 UAV sprayers for a typical tropical crop, coconut. In this study, the spray drift distribution of droplets on the ground and in the air, as well as the bystander exposure risk, were comparatively analyzed using droplet size (VF, M, and C) and spray volume (75 L/hm2 and 60 L/hm2) as trial variables. The results indicated that the spray drift characteristics of UAV sprayers were significantly affected by droplet size and spray volume. The spray drift percentage was negatively correlated with the downwind distance and the sampling height. The smaller the droplet size, the farther the buffer zone distance, up to more than 30 m, and the cumulative drift percentage is also greater, resulting in a significant risk of spray drift. The reduction in spray volume helped to reduce the spray drift, and the cumulative drift percentage was reduced by 73.87 % with a droplet size of M. The region of the body where spray drift is deposited the most on bystanders is near chest height. This study provides a reference for the rational and safe use of multirotor UAV sprayers application operations in the tropics and enriches the spray drift database in the tropics.

Drones can be used to provide dispatch centres with on-site photos before arrival of EMS in time critical incidents.

BACKGROUND: Drones are able to deliver automated external defibrillators in cases of out-of-hospital cardiac arrest (OHCA) but can be deployed for other purposes. Our aim was to evaluate the feasibility of sending live photos to dispatch centres before arrival of other units during time-critical incidents. METHODS: In this retrospective observational study, the regional dispatch centre implemented a new service using five existing AED-drone systems covering an estimated 200000 inhabitants in Sweden. Drones were deployed automatically over a 4-month study period (December 2022-April 2023) in emergency calls involving suspected OHCAs, traffic accidents and fires in buildings. Upon arrival at the scene, an overhead photo was taken and transmitted to the dispatch centre. Feasibility of providing photos in real time, and time delays intervals were examined. RESULTS: Overall, drones were deployed in 59/440 (13%) of all emergency calls: 26/59 (44%) of suspected OHCAs, 20/59 (34%) of traffic accidents, and 13/59 (22%) of fires in buildings. The main reasons for non-deployment were closed airspace and unfavourable weather conditions (68%). Drones arrived safely at the exact location in 58/59 cases (98%). Their overall median response time was 3:49 min, (IQR 3:18-4:26) vs. emergency medical services (EMS), 05:51 (IQR: 04:29-08:04) p-value for time difference between drone and EMS = 0,05. Drones arrived first on scene in 47/52 cases (90%) and the largest median time difference was found in suspected OHCAs 4:10 min, (IQR: 02:57-05:28). The time difference in the 5/52 (10%) cases when EMS arrived first the time difference was 5:18 min (IQR 2:19-7:38), p = NA. Photos were transmitted correctly in all 59 alerts. No adverse events occurred. CONCLUSION: In a newly implemented drone dispatch service, drones were dispatched to 13% of relevant EMS calls. When drones were dispatched, they arrived at scene earlier than EMS services in 90% of cases. Drones were able to relay photos to the dispatch centre in all cases. Although severely affected by closed airspace and weather conditions, this novel method may facilitate additional decision-making information during time-critical incidents.

Identifying rice field weeds from unmanned aerial vehicle remote sensing imagery using deep learning.

BACKGROUND: Rice field weed object detection can provide key information on weed species and locations for precise spraying, which is of great significance in actual agricultural production. However, facing the complex and changing real farm environments, traditional object detection methods still have difficulties in identifying small-sized, occluded and densely distributed weed instances. To address these problems, this paper proposes a multi-scale feature enhanced DETR network, named RMS-DETR. By adding multi-scale feature extraction branches on top of DETR, this model fully utilizes the information from different semantic feature layers to improve recognition capability for rice field weeds in real-world scenarios. METHODS: Introducing multi-scale feature layers on the basis of the DETR model, we conduct a differentiated design for different semantic feature layers. The high-level semantic feature layer adopts Transformer structure to extract contextual information between barnyard grass and rice plants. The low-level semantic feature layer uses CNN structure to extract local detail features of barnyard grass. Introducing multi-scale feature layers inevitably leads to increased model computation, thus lowering model inference speed. Therefore, we employ a new type of Pconv (Partial convolution) to replace traditional standard convolutions in the model. RESULTS: Compared to the original DETR model, our proposed RMS-DETR model achieved an average recognition accuracy improvement of 3.6% and 4.4% on our constructed rice field weeds dataset and the DOTA public dataset, respectively. The average recognition accuracies reached 0.792 and 0.851, respectively. The RMS-DETR model size is 40.8 M with inference time of 0.0081 s. Compared with three classical DETR models (Deformable DETR, Anchor DETR and DAB-DETR), the RMS-DETR model respectively improved average precision by 2.1%, 4.9% and 2.4%. DISCUSSION: This model is capable of accurately identifying rice field weeds in complex real-world scenarios, thus providing key technical support for precision spraying and management of variable-rate spraying systems.

Optimizing UAV spray parameters to improve precise control of tobacco pests at different growth stages.

BACKGROUND: Unmanned aerial vehicles (UAVs) for pesticide application show promising potential in tobacco pest management. However, the impact of flight parameters on spray efficacy requires further investigation. Three field experiments were conducted from the rosette to the maturation stage of tobacco to systematically assess spray efficacy under varying flight heights, speeds, and application volumes. Using a multi-index weight analysis method, optimal operational parameter combinations for different tobacco growth stages were evaluated and compared with backpack electric sprayers. RESULTS: For the rosette stage, the recommended parameter is a flight speed of 5 m s-1, a flight height of 2 m, and a liquid application volume of 30 L hm-2; during the vigorous growth stage, the suggested parameter includes a flight speed of 3 m s-1, a flight height of 2 m, and a liquid application volume of 22.5 L hm-2. In the maturing stage, optimal parameter consists of a flight speed of 3 m s-1, a flight height of 3.5 m, and a liquid application volume of 30 L hm-2. Furthermore, UAV spraying achieves higher droplet deposition on both sides of tobacco leaves compared to traditional electric backpack sprayers. CONCLUSIONS: Adjusting UAV spraying parameters for different tobacco growth stages is crucial. These results can provide the methods for the precise control technology of tobacco pests at different growth stages. © 2024 Society of Chemical Industry.

HeMoDU: High-Efficiency Multi-Object Detection Algorithm for Unmanned Aerial Vehicles on Urban Roads.

Unmanned aerial vehicle (UAV)-based object detection methods are widely used in traffic detection due to their high flexibility and extensive coverage. In recent years, with the increasing complexity of the urban road environment, UAV object detection algorithms based on deep learning have gradually become a research hotspot. However, how to further improve algorithmic efficiency in response to the numerous and rapidly changing road elements, and thus achieve high-speed and accurate road object detection, remains a challenging issue. Given this context, this paper proposes the high-efficiency multi-object detection algorithm for UAVs (HeMoDU). HeMoDU reconstructs a state-of-the-art, deep-learning-based object detection model and optimizes several aspects to improve computational efficiency and detection accuracy. To validate the performance of HeMoDU in urban road environments, this paper uses the public urban road datasets VisDrone2019 and UA-DETRAC for evaluation. The experimental results show that the HeMoDU model effectively improves the speed and accuracy of UAV object detection.

Downlink Transmissions of UAV-RIS-Assisted Cell-Free Massive MIMO Systems: Location and Trajectory Optimization.

In this paper, we investigate a cell-free massive multiple-input multiple-output (CF-mMIMO) system with a reconfigurable intelligent surface (RIS) carried by an unmanned aerial vehicle (UAV), called the UAV-RIS. Compared with the RIS located on the ground, the UAV-RIS has a wider coverage that can reflect all signals from access points (APs) and user equipment (UE). By correlating the UAV location with the Rician K-factor, we derive a closed-form approximation of the UE achievable downlink rate. Based on this, we obtain the optimal UAV location and RIS phase shift that can maximize the UE sum rate through an alternating optimization method. Simulation results have verified the accuracy of the derived approximation and shown that the UE sum rate can be significantly improved with the obtained optimal UAV location and RIS phase shift. Moreover, we find that with a uniform UE distribution, the UAV-RIS should fly to the center of the system, while with an uneven UE distribution, the UAV-RIS should fly above the area where UEs are gathered. In addition, we also design the best trajectory for the UAV-RIS to fly from its initial location to the optimal destination while maintaining the maximum UE sum rate per time slot during the flight.

Unmanned Aerial Vehicle-Assisted Terahertz-Visible Light Communication Systems: An In-Depth Performance Analysis.

This paper investigates the performance of dual-hop unmanned aerial vehicle (UAV)-assisted communication channels, employing a decode-and-forward (DF) relay architecture. The system leverages terahertz (THz) communication for the primary hop and visible light communication (VLC) for the secondary hop. We conduct an in-depth analysis by deriving closed-form expressions for the end-to-end (E2E) bit error rate (BER). Additionally, we use a Monte Carlo simulation approach to generate best-fitting curves, validating our analytical expressions. A performance evaluation through BER and outage probability metrics demonstrates the effectiveness of the proposed system. Specifically, our results indicate that the proposed system outperforms Free-Space Optics (FSO)-VLC and Radio-Frequency (RF)-VLC at a higher signal-to-noise ratio (SNR). The results of this study provide valuable insights into the feasibility and limitations of UAV-assisted THz-VLC communication systems.

Surface Defect-Extended BIM Generation Leveraging UAV Images and Deep Learning.

Defect inspection of existing buildings is receiving increasing attention for digitalization transfer in the construction industry. The development of drone technology and artificial intelligence has provided powerful tools for defect inspection of buildings. However, integrating defect inspection information detected from UAV images into semantically rich building information modeling (BIM) is still challenging work due to the low defect detection accuracy and the coordinate difference between UAV images and BIM models. In this paper, a deep learning-based method coupled with transfer learning is used to detect defects accurately; and a texture mapping-based defect parameter extraction method is proposed to achieve the mapping from the image U-V coordinate system to the BIM project coordinate system. The defects are projected onto the surface of the BIM model to enrich a surface defect-extended BIM (SDE-BIM). The proposed method was validated in a defect information modeling experiment involving the No. 36 teaching building of Nantong University. The results demonstrate that the methods are widely applicable to various building inspection tasks.

Drone-Borne Magnetic Gradiometry in Archaeological Applications.

The use of magnetometers arranged in a gradiometer configuration offers a practical and widely used solution, particularly in archaeological applications where the sources of interest are generally shallow. Since magnetic anomalies due to archaeological remains often have low amplitudes, highly sensitive magnetic sensors are kept very close to the ground to reveal buried structures. However, the deployment of Unmanned Aerial Vehicles (UAVs) is increasingly becoming a reliable and valuable tool for the acquisition of magnetic data, providing uniform coverage of large areas and access to even very steep terrain, saving time and reducing risks. However, the application of a vertical gradiometer for drone-borne measurements is still challenging due to the instability of the system drone magnetometer in flight and noise issues due to the magnetic interference of the mobile platform or related to the oscillation of the suspended sensors. We present the implementation of a magnetic vertical gradiometer UAV system and its use in an archaeological area of Southern Italy. To reduce the magnetic and electromagnetic noise caused by the aircraft, the magnetometer was suspended 3m below the drone using ropes. A Continuous Wavelet Transform analysis of data collected in controlled tests confirmed that several characteristic power spectrum peaks occur at frequencies compatible with the magnetometer oscillations. This noise was then eliminated with a properly designed low-pass filter. The resulting drone-borne vertical gradient data compare very well with ground-based magnetic measurements collected in the same area and taken as a control dataset.

Dynamic UAV Deployment Scheme Based on Edge Computing for Forest Fire Scenarios.

This study investigates the dynamic deployment of unmanned aerial vehicles (UAVs) using edge computing in a forest fire scenario. We consider the dynamically changing characteristics of forest fires and the corresponding varying resource requirements. Based on this, this paper models a two-timescale UAV dynamic deployment scheme by considering the dynamic changes in the number and position of UAVs. In the slow timescale, we use a gate recurrent unit (GRU) to predict the number of future users and determine the number of UAVs based on the resource requirements. UAVs with low energy are replaced accordingly. In the fast timescale, a deep-reinforcement-learning-based UAV position deployment algorithm is designed to enable the low-latency processing of computational tasks by adjusting the UAV positions in real time to meet the ground devices' computational demands. The simulation results demonstrate that the proposed scheme achieves better prediction accuracy. The number and position of UAVs can be adapted to resource demand changes and reduce task execution delays.

An Improved Instance Segmentation Method for Fast Assessment of Damaged Buildings Based on Post-Earthquake UAV Images.

Quickly and accurately assessing the damage level of buildings is a challenging task for post-disaster emergency response. Most of the existing research mainly adopts semantic segmentation and object detection methods, which have yielded good results. However, for high-resolution Unmanned Aerial Vehicle (UAV) imagery, these methods may result in the problem of various damage categories within a building and fail to accurately extract building edges, thus hindering post-disaster rescue and fine-grained assessment. To address this issue, we proposed an improved instance segmentation model that enhances classification accuracy by incorporating a Mixed Local Channel Attention (MLCA) mechanism in the backbone and improving small object segmentation accuracy by refining the Neck part. The method was tested on the Yangbi earthquake UVA images. The experimental results indicated that the modified model outperformed the original model by 1.07% and 1.11% in the two mean Average Precision (mAP) evaluation metrics, mAPbbox50 and mAPseg50, respectively. Importantly, the classification accuracy of the intact category was improved by 2.73% and 2.73%, respectively, while the collapse category saw an improvement of 2.58% and 2.14%. In addition, the proposed method was also compared with state-of-the-art instance segmentation models, e.g., Mask-R-CNN and YOLO V9-Seg. The results demonstrated that the proposed model exhibits advantages in both accuracy and efficiency. Specifically, the efficiency of the proposed model is three times faster than other models with similar accuracy. The proposed method can provide a valuable solution for fine-grained building damage evaluation.