Abstract: In recent years, People pay more and more attention to garbage classification and recycling, but garbage classification consumes a lot of manpower and material resources and the sorting efficiency is low.  To solve the problem that the garbage detection method based on rectangular bounding box is not effective enough when applied to multi-classification environment, a garbage detection method based on improved Mask R-CNN is proposed. Instead of the traditional ResNet, this method uses the improved ResNeXt101 as the backbone network for feature extraction, which improves the accuracy of object detection and the accuracy of background boundary segmentation. Experimental results show that compared with the traditional Mask R-CNN model, the proposed model’s average classification accuracy is 91.1%, improved by 2.35%. Finally, the experimental comparison with the current popular object detection algorithms shows that the classification accuracy and segmentation accuracy of the proposed algorithm are excellent, which proves the feasibility and effectiveness of the proposed method in the garbage detection task.

Key words: garbage classification, target detection, regions with CNN features