Aiming at the problem that the irregular shape of the milling machine debris leads to the unclear outline of the debris and the low segmentation accuracy in the image segmentation, this paper proposes an improved DeepLabV3+ milling machine debris segmentation algorithm. First,the CBAM model is embedded in the Xcepetion module of DeepLabV3+ to optimize the weight and position information of the channel,and to strengthen the feature learning of the debris image area.Secondly,the ASPP module of DeepLabV3+ is changed to the dense connection method to increase the receptive field of the feature points of the debris image.The feature reuse efficiency of the milling machine debris image is improved.Finally,the multi-scale adaptive feature fusion method is adopted in the decoding process,and the multi-scale features are aggregated as the input features of the decoder to improve the segmentation accuracy and robustness of the debris image.The experimental results show that the algorithm in this paper is better than other segmentation algorithms.Compared with DeepLabV3+,the improved algorithm improves the pixel accuracy by 0.026,mean intersection over union (MIOU) by 0.020,and the F₁-measure by 0.013.