YOLOV5s object detection based on Sim SPPF hybrid pooling
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Abstract:
Aiming at the problem of low surface defect detection accuracy of industrial products, an object detection method based on Sim SPPF hybrid pooling improved YOLOV5s model is proposed. The algorithm introduces Channel Attention module, Simplified Spatial Pyramid Pooling Fast Feature Vector Pyramid and Efficient Intersection Over Union loss function. Feature vector pyramids fuse high-dimensional and low-dimensional features, which makes semantic information richer. The channel attention mechanism performs maximum pooling and average pooling operations on the feature map. Hybrid pooling comprehensively improves detection computing efficiency and accurate deployment ability. The results show that the improved YOLOV5s model is better than the original YOLOV5s model, the average test accuracy (mAP) can reach 91.8%, the average accuracy (mAP) of the model can be increased by 17.4%, the detection speed can reach 108 FPS, the detection speed can be increased by 18 FPS, and the improved model is practicable and the overall performance is better than other conventional models.
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Project Supported:
[2]国家重点研发计划(2022YFF0706000)
Clc Number:
