ABSTRACT
In the field of the Internet of Things, image acquisition equipment is the very important equipment, which will generate lots of invalid data during real-time monitoring. Analyzing the data collected directly from the terminal by edge calculation, we can remove invalid frames and improve the accuracy of system detection. SSD algorithm has a relatively light and fast detection speed. However, SSD algorithm do not take full advantage of both shallow and deep information of data. So a multiscale feature fusion attention mechanism structure based on SSD algorithm has been proposed in this paper, which combines multiscale feature fusion and attention mechanism. The adjacent feature layers for each detection layer are fused to improve the feature information expression ability. Then, the attention mechanism is added to increase the attention of the feature map channels. The results of the experiments show that the detection accuracy of the optimized model is improved, and the reliability of edge calculation has been improved.
ABSTRACT
Bioinspired object detection in remotely sensed images plays an important role in a variety of fields. Due to the small size of the target, complex background information, and multi-scale remote sensing images, the generalized YOLOv5 detection framework is unable to obtain good detection results. In order to deal with this issue, we proposed YOLO-DRS, a bioinspired object detection algorithm for remote sensing images incorporating a multi-scale efficient lightweight attention mechanism. First, we proposed LEC, a lightweight multi-scale module for efficient attention mechanisms. The fusion of multi-scale feature information allows the LEC module to completely improve the model's ability to extract multi-scale targets and recognize more targets. Then, we propose a transposed convolutional upsampling alternative to the original nearest-neighbor interpolation algorithm. Transposed convolutional upsampling has the potential to greatly reduce the loss of feature information by learning the feature information dynamically, thereby reducing problems such as missed detections and false detections of small targets by the model. Our proposed YOLO-DRS algorithm exhibits significant improvements over the original YOLOv5s. Specifically, it achieves a 2.3% increase in precision (P), a 3.2% increase in recall (R), and a 2.5% increase in mAP@0.5. Notably, the introduction of the LEC module and transposed convolutional results in a respective improvement of 2.2% and 2.1% in mAP@0.5. In addition, YOLO-DRS only increased the GFLOPs by 0.2. In comparison to the state-of-the-art algorithms, namely YOLOv8s and YOLOv7-tiny, YOLO-DRS demonstrates significant improvements in the mAP@0.5 metrics, with enhancements ranging from 1.8% to 7.3%. It is fully proved that our YOLO-DRS can reduce the missed and false detection problems of remote sensing target detection.
ABSTRACT
In order to solve the problem that deep learning-based flower image classification methods lose more feature information in the early feature extraction process, and the model takes up more storage space, a new lightweight neural network model based on multi-scale feature fusion and attention mechanism is proposed in this paper. First, the AlexNet model is chosen as the basic framework. Second, a multi-scale feature fusion module (MFFM) is used to replace the shallow single-scale convolution. MFFM, which contains three depthwise separable convolution branches with different sizes, can fuse features with different scales and reduce the feature loss caused by single-scale convolution. Third, two layers of improved Inception module are first added to enhance the extraction of deep features, and a layer of hybrid attention module is added to strengthen the focus of the model on key information at a later stage. Finally, the flower image classification is completed using a combination of global average pooling and fully connected layers. The experimental results demonstrate that our lightweight model has fewer parameters, takes up less storage space and has higher classification accuracy than the baseline model, which helps to achieve more accurate flower image recognition on mobile devices.
ABSTRACT
Through the functional combination of relevant departments involved in hospital procurement, to simplify and unify the work process, we establish a standardized procurement system, to realize the pre-procurement budget and approval, power balance, strengthen the fairness and openness of procurement process. By introducing the closed-loop process of in-process supervision to ensure the impartiality of review and post-evaluation control, it comprehensively strengthens the internal control of procurement management, and finally realizes the purpose of strengthening procurement risk prevention and procurement quality management.
Subject(s)
Hospitals, Public , Purchasing, Hospital , Quality Control , ResearchABSTRACT
Existing classification methods tend not to work well on various error diffusion patterns. Thus a novel classification method for halftone image via statistics matrices is proposed. The statistics matrix descriptor of halftone image is constructed according to the characteristic of error diffusion filters. On this basis, an extraction algorithm is developed based on halftone image patches. The feature modeling is formulated as an optimization problem and then a gradient descent method is used to seek optimum class feature matrices by minimizing the total square error. A maximum likelihood method is proposed according to priori knowledge of training samples. In experiments, the performance evaluation method is provided and comparisons of performance between our method and seven similar methods are made. Then, the influence of parameters, performance under various attacks, computational time complexity and the limitations are discussed. From our experimental study, it is observed that our method has lower classification error rate when compared with other similar methods. In addition, it is robust against usual attacks.
