As global environmental awareness rose and the importance of resource recycling became increasingly prominent,the accurate identification of recyclable waste is recognized as a crucial task.A method for recyclable waste identification based on the attention mechanism was proposed.This method utilized the Mask R-CNN network model to extract features such as texture,shape,and color from waste images.At the same time,the ECA attention mechanism was introduced,allowing the model,through learning attention weights,to focus more selectively on those features most closely related to waste classification.Additionally,the GIoU loss function was employed to better reflect the relative position and size relationships between bounding boxes.Experimental verification showed that the improved model achieved an mAP of 81.7%,representing an 8.5%increase compared to the original model.
维普
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