In response to the growing demand for real-time mobile object detection deployment,the current YOLO backbone net-work falls short.Hence,this paper proposes YOLOLW,a lightweight object detection model based on the anchor frame.Firstly,it incorporates a novel lightweight decoupling header to enhance focus on classification and regression tasks and improve model accuracy.Secondly,it designs a lightweight and reparameterized network structure that achieves superior detection accuracy while maintaining its lightweight nature.Thirdly,it enhances the feature pyramid structure(FPN)by effectively integrating shal-low features through dynamic convolution and cross-hierarchy association.Lastly,spatial and channel attention mechanisms are introduced to significantly boost the model's accuracy.Experimental results validate the effectiveness of the YOLOLW model.
维普
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