Research on Detection Algorithm of Floating Objects on Water Surface Based on Improved YOLOv5s
The water surface image has complex features such as water wave disturbances,light reflection and shoreline reflections,which cause existing object detection algorithms to fail to recognize floating objects.Therefore,a water surface floating object detection algorithm based on an improved YOLOv5s was proposed.By modifying the neck structure of the network,adding object detection layers to improve the detection accuracy of the feature extraction network for multi-scale targets;introducing the parameter-free attention mechanism SimAM in the feature fusion layer to enhance the model's feature extraction ability,and using the CARAFE upsampling method to enhance the network's receptive field and improve the perception of detailed features;integrating the ConvMixer Layer into the YOLOv5s network structure,the model's running speed was improved while maintaining detection accuracy and reducing the model's parameter count.The experimental re-sults show that the improved model has good detection performance in real samples,with a mean average precision of 97.1%,which is 4.9%higher than the original YOLOv5s model,and can effectively improve the issue of missed detection and false detection of floating objects on the water surface.
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