Real-time path planning for autonomous vehicle off-road driving.

Background: Autonomous driving is a growing research area that brings benefits in science, economy, and society. Although there are several studies in this area, currently there is no a fully autonomous vehicle, particularly, for off-road navigation. Autonomous vehicle (AV) navigation is a complex process based on application of multiple technologies and algorithms for data acquisition, management and understanding. Particularly, a self-driving assistance system supports key functionalities such as sensing and terrain perception, real time vehicle mapping and localization, path prediction and actuation, communication and safety measures, among others. Methods: In this work, an original approach for vehicle autonomous driving in off-road environments that combines semantic segmentation of video frames and subsequent real-time route planning is proposed. To check the relevance of the proposal, a modular framework for assistive driving in off-road scenarios oriented to resource-constrained devices has been designed. In the scene perception module, a deep neural network is used to segment Red-Green-Blue (RGB) images obtained from camera. The second traversability module fuses Light Detection And Ranging (LiDAR) point clouds with the results of segmentation to create a binary occupancy grid map to provide scene understanding during autonomous navigation. Finally, the last module, based on the Rapidly-exploring Random Tree (RRT) algorithm, predicts a path. The Freiburg Forest Dataset (FFD) and RELLIS-3D dataset were used to assess the performance of the proposed approach. The theoretical contributions of this article consist of the original approach for image semantic segmentation fitted to off-road driving scenarios, as well as adapting the shortest route searching A* and RRT algorithms to AV path planning. Results: The reported results are very promising and show several advantages compared to previously reported solutions. The segmentation precision achieves 85.9% for FFD and 79.5% for RELLIS-3D including the most frequent semantic classes. While compared to other approaches, the proposed approach is faster regarding computational time for path planning.

Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection.

This paper explores a data augmentation approach for images of rigid bodies, particularly focusing on electrical equipment and analogous industrial objects. By leveraging manufacturer-provided datasheets containing precise equipment dimensions, we employed straightforward algorithms to generate synthetic images, permitting the expansion of the training dataset from a potentially unlimited viewpoint. In scenarios lacking genuine target images, we conducted a case study using two well-known detectors, representing two machine-learning paradigms: the Viola-Jones (VJ) and You Only Look Once (YOLO) detectors, trained exclusively on datasets featuring synthetic images as the positive examples of the target equipment, namely lightning rods and potential transformers. Performances of both detectors were assessed using real images in both visible and infrared spectra. YOLO consistently demonstrates F1 scores below 26% in both spectra, while VJ's scores lie in the interval from 38% to 61%. This performance discrepancy is discussed in view of paradigms' strengths and weaknesses, whereas the relatively high scores of at least one detector are taken as empirical evidence in favor of the proposed data augmentation approach.

A video processing and machine vision-based automatic analyzer to determine sequentially total suspended and settleable solids in wastewater.

The monitoring of total suspended (TSS) and settleable (SetS) solids in wastewater is essential to maintain the quality parameters for aquatic biota because they can transport pollutants and block light penetration. Determining them by their respective reference methods, however, is laborious, expensive, and time consuming. To overcome this, we developed a new analytical instrument called Solids in Wastewater's Machine Vision-based Automatic Analyzer (SWAMVA), which is equiped with an automatic sampler and a software for real-time digital movie capture to quantify sequentially the TSS and SetS contents in wastewater samples. The machine vision algorithm (MVA) coupled with the Red color plane (derived from color histograms in the Red-Green-Blue (RGB) system) showed the best prediction results with R2 of 0.988 and 0.964, and relative error of prediction (REP) of 6.133 and 9.115% for TSS and SetS, respectively. The constructed models were validated by Analysis of Variance (ANOVA), and the accuracy and precision of the predictions by the t- and F-tests, respectively, at a 0.05 significance level. The elliptical joint confidence region (EJCR) test confirmed the accuracy, while the coefficient of variation (CV) of 6.529 and 10.908% confirmed the good precisions, respectively. Compared with the reference method (Standard Methods For the Examination of Water and Wastewater), the proposed method reduced the analysis volume from 1.5 L to just 15 mL and the analysis time from 12 h to 24 s per sample. Therefore, SWAMVA can be considered an important alternative to the determination of TSS and SetS in wastewater as an automatic, fast, and low-cost analytical tool, following the principles of Green Chemistry and exploiting Industry 4.0 features such as intelligent processing, miniaturization, and machine vision.

Estimation of genetic parameters for body areas in Nile tilapia measured by digital image analysis.

Digital image analysis is a practical, non-invasive, and relatively low-cost tool that may assist in the evaluation of body traits in Nile tilapia, being particularly useful for assessing difficult-to-measure variables, such as body areas. In this study, we aimed to estimate variance components and genetic parameters for body areas of Nile tilapia obtained by digital images. The data set comprised body weight (BW) records of 1,917 pond-reared fish at 366 days of age. Of this total, 656 animals were photographed and subjected to image analysis of trunk area (TA), head area (HA), caudal fin area (CFA) and fillet area (FA). Heritabilities and genetic correlations were estimated through multiple-trait models based on Bayesian inference. Heritability estimates for BW, TA, HA, CFA and FA were 0.25, 0.23, 0.26, 0.21 and 0.25, respectively. Genetic correlations between the traits were high and positive, ranging from 0.70 to 0.98. We highlight the genetic correlation between BW and TA (rG  = 0.98) and FA (rG  = 0.97). In view of the observed results, it can be concluded that trunk and fillet areas obtained by digital image analysis can lead to indirect genetic gains in weight and other body areas. In addition, the areas studied have potential as a selection criterion and may assist in studies on changes in the body shape in Nile tilapia.

Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging.

Conventional methods for detecting seed-borne fungi are laborious and time-consuming, requiring specialized analysts for characterization of pathogenic fungi on seed. Multispectral imaging (MSI) combined with machine vision was used as an alternative method to detect Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in black oat seeds (Avena strigosa Schreb). The seeds were inoculated with Drechslera avenae (D. avenae) and then incubated for 24, 72 and 120 h. Multispectral images of non-infested and infested seeds were acquired at 19 wavelengths within the spectral range of 365 to 970 nm. A classification model based on linear discriminant analysis (LDA) was created using reflectance, color, and texture features of the seed images. The model developed showed high performance of MSI in detecting D. avenae in black oat seeds, particularly using color and texture features from seeds incubated for 120 h, with an accuracy of 0.86 in independent validation. The high precision of the classifier showed that the method using images captured in the Ultraviolet A region (365 nm) could be easily used to classify black oat seeds according to their health status, and results can be achieved more rapidly and effectively compared to conventional methods.

Mature pomegranate recognition methods in natural environments using machine vision / Métodos de reconhecimento de romã madura em ambientes naturais usando visão de máquina

ABSTRACT: The use of machine vision to recognize mature pomegranates in natural environments is of major significance in improving the applicability and work efficiency of picking robots. By analyzing the color characteristics of color images of mature pomegranates under different illumination conditions, the feasibility of the YCbCr color model for pomegranate image recognition under different illumination conditions was proven. First, the Cr component map of pomegranate image is selected and then the pomegranate fruit is segmented by the kernel fuzzy C-means clustering algorithm to obtain the pomegranate image. Contrast experiments of pomegranate image segmentation under different illumination conditions were then performed using the proposed kernel fuzzy C-means clustering algorithm, the fuzzy C-means clustering algorithm, the Otsu algorithm and the threshold segmentation algorithm. Results of the experiments verified the effectiveness and superiority of the proposed algorithm.

RESUMO: O uso de máquina para reconhecer romãs maduras em ambientes naturais é de grande importância para melhorar a aplicabilidade e a eficiência do trabalho de robôs de colheita. Ao analisar as características de cor das imagens coloridas de romãs maduras sob diferentes condições de iluminação, a viabilidade do modelo de cores YCbCr para o reconhecimento de imagens de romãs sob diferentes condições de iluminação foi comprovada. Primeiro, o mapa do componente Cr da imagem da romã é selecionado e, em seguida, o fruto da romãzeira é segmentado pelo algoritmo de agrupamento C-means fuzzy do kernel para obter a imagem da romã. Experimentos contrastados de segmentação de imagens de romã sob diferentes condições de iluminação foram então realizados usando o algoritmo proposto de agrupamento C-means fuzzy, o algoritmo fuzzy de agrupamento C-means, o algoritmo Otsu e o algoritmo de segmentação de limiares. Os resultados dos experimentos verificaram a efetividade e superioridade do algoritmo proposto.

Mature pomegranate recognition methods in natural environments using machine vision / Métodos de reconhecimento de romã madura em ambientes naturais usando visão de máquina

The use of machine vision to recognize mature pomegranates in natural environments is of major significance in improving the applicability and work efficiency of picking robots. By analyzing the color characteristics of color images of mature pomegranates under different illumination conditions, the feasibility of the YCbCr color model for pomegranate image recognition under different illumination conditions was proven. First, the Cr component map of pomegranate image is selected and then the pomegranate fruit is segmented by the kernel fuzzy C-means clustering algorithm to obtain the pomegranate image. Contrast experiments of pomegranate image segmentation under different illumination conditions were then performed using the proposed kernel fuzzy C-means clustering algorithm, the fuzzy C-means clustering algorithm, the Otsu algorithm and the threshold segmentation algorithm. Results of the experiments verified the effectiveness and superiority of the proposed algorithm.(AU)

O uso de máquina para reconhecer romãs maduras em ambientes naturais é de grande importância para melhorar a aplicabilidade e a eficiência do trabalho de robôs de colheita. Ao analisar as características de cor das imagens coloridas de romãs maduras sob diferentes condições de iluminação, a viabilidade do modelo de cores YCbCr para o reconhecimento de imagens de romãs sob diferentes condições de iluminação foi comprovada. Primeiro, o mapa do componente Cr da imagem da romã é selecionado e, em seguida, o fruto da romãzeira é segmentado pelo algoritmo de agrupamento C-means fuzzy do kernel para obter a imagem da romã. Experimentos contrastados de segmentação de imagens de romã sob diferentes condições de iluminação foram então realizados usando o algoritmo proposto de agrupamento C-means fuzzy, o algoritmo fuzzy de agrupamento C-means, o algoritmo Otsu e o algoritmo de segmentação de limiares. Os resultados dos experimentos verificaram a efetividade e superioridade do algoritmo proposto.(AU)

Sensor Fusion of a Mobile Device to Control and Acquire Videos or Images of Coffee Branches and for Georeferencing Trees.

Smartphones show potential for controlling and monitoring variables in agriculture. Their processing capacity, instrumentation, connectivity, low cost, and accessibility allow farmers (among other users in rural areas) to operate them easily with applications adjusted to their specific needs. In this investigation, the integration of inertial sensors, a GPS, and a camera are presented for the monitoring of a coffee crop. An Android-based application was developed with two operating modes: (i) Navigation: for georeferencing trees, which can be as close as 0.5 m from each other; and (ii) Acquisition: control of video acquisition, based on the movement of the mobile device over a branch, and measurement of image quality, using clarity indexes to select the most appropriate frames for application in future processes. The integration of inertial sensors in navigation mode, shows a mean relative error of ±0.15 m, and total error ±5.15 m. In acquisition mode, the system correctly identifies the beginning and end of mobile phone movement in 99% of cases, and image quality is determined by means of a sharpness factor which measures blurriness. With the developed system, it will be possible to obtain georeferenced information about coffee trees, such as their production, nutritional state, and presence of plagues or diseases.