RETINANET-BASED DIRECTED TARGET DETECTION FOR RECYCLABLE WASTE
To realize the automation of waste sorting,the research of vision-based automatic detection of recyclable waste is of great importance.To realize the automation of waste sorting,the traditional horizontal frame target detection algorithm loses the directional information of the target during the detection,and the overlap of the positioning frame is serious so that the true length and width of the target cannot be obtained,which is unfavorable to the subsequent sorting.The algorithm is based on the improvement of the RetinaNet network,adding the angle prediction module in the detection head,using the PSC angle encoder to improve the angle return boundary problem,introducing the Balanced L1 loss function to balance the gradient contribution of simple and difficult samples,and replacing the backbone network with the Swin Transformer to enhance the feature extraction capability of the network.The network with angle prediction can locate the garbage more accurately,and the improved network accuracy(mAP)reaches 78.4％,which is 12 percentage points higher than the original algorithm,while the detection effect of PSC is better than other methods compared with other angle encoders.
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