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The deep learning instance segmentation algorithm YOLACT was improved to solve the problem of missing detection of densely stacked packing boxes and the problem of difficult to capture accurately due to segmenting the inexact edges of each packing box. Firstly, the industrial camera was used to collect the stacking image of the packing box, and then the Labelme was used to annotate image to create the dataset, and the dataset was expanded through the data enhancement method. Then, in order to improve the segmentation accuracy of the model, the Canny edge detection operator was used for the groundtruth value and predicted mask of the prototype mask output branch (Protonet) in YOLACT, and the binary cross-entropy loss of the them was added to the original network as a loss function. Finally, the trained optimal model was used to test the image data of the test set. The results show that the improved model predicted mask mAP_0.5:0.95 can reach 0.543, which is 2.2% higher than the original model. At the same time, the segmentation accuracy of the packing box edge has also been improved to a certain extent. The model’s inference speed can reach 10.2 frames/second, which can meet the accuracy requirements and production beat requirements.