Application and Performance Analysis of Multi Object Detection in Hazardous Areas Based on YOLOv5
In order to improve the efficiency and accuracy of multi-target detection in hazardous areas,based on the YOLOv5 algorithm,through in-depth analysis of algorithm principles and optimization strategies,Qt developed a multi-target detection system for hazardous areas based on monocular vision and object detection algorithm YOLOv5,achieving efficient detection of multiple targets in hazardous areas.In the study,the YOLOv5 algorithm was optimized by improving the model architecture,pruning,and quantization to meet the speed and efficiency requirements of multi-target detection in hazardous areas.The optimized YOLOv5 algorithm has significant performance improvement,with an average accuracy of 97.3%and a speed increase of nearly 10%,providing strong technical support for multi-target detection applications in hazardous areas.
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