A Multiscale Fusion YOLOV3-Based Model for Human Abnormal Behavior Detection in Special Scenarios

Li, Zhihong; Zhang, Jing; Wen, Yanjie; Yang, Dong; Xu, Wangtu

doi:10.1061/jtepbs.teeng-7130

Cited by 4 publications

(1 citation statement)

References 44 publications

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“…Multi-scale training is applied to faster RCNN to enhance the robustness of the network for airport detection of different scales. The core idea of YOLO is to transform the target detection into a regression problem [14,15], using the whole map as the input of the network, just after a neural network, so that YOLO uses the whole graph as the input to the network, and just goes through a neural network to obtain the position of the bounding box and its category. Wang et al [16] investigated the improved YOLOv4 algorithm using a shallow feature enhancement mechanism for the problems of insensitivity to small objects and low detection accuracy in traffic light detection and recognition.…”

Section: Introductionmentioning

confidence: 99%

Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

Meng,

Cui,

Liu

et al. 2023

Processes

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Under the background of intelligent manufacturing, in order to solve the complex problems of manual detection of metallurgical saw blade defects in enterprises, such as real-time detection, false detection, and the detection model being too large to deploy, a study on a metallurgical saw blade surface defect detection algorithm based on SC-YOLOv5 is proposed. Firstly, the SC network is built by integrating coordinate attention (CA) into the Shufflenet-V2 network, and the backbone network of YOLOv5 is replaced by the SC network to improve detection accuracy. Then, the SIOU loss function is used in the YOLOv5 prediction layer to solve the angle problem between the prediction frame and the real frame. Finally, in order to ensure both accuracy and speed, lightweight convolution (GSConv) is used to replace the ordinary convolution module. The experimental results show that the mAP@0.5 of the improved YOLOv5 model is 88.5%, and the parameter is 31.1M. Compared with the original YOLOv5 model, the calculation amount is reduced by 56.36%, and the map value is increased by 0.021. In addition, the overall performance of the improved SC-YOLOv5 model is better than that of the SSD and YOLOv3 target detection models. This method not only ensures the high detection rate of the model, but also significantly reduces the complexity of the model and the amount of parameter calculation. It meets the needs of deploying mobile terminals and provides an effective reference direction for applications in enterprises.

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Section: Introductionmentioning

confidence: 99%