复杂堆叠场景下的垃圾实例分割受到严重遮挡和高密集性特点的影响，具有更大的检测难度。针对该问题，提出了一种结合DCT-Mask和双层特征融合网络思想的实例分割方法，用于高度堆叠场景下的垃圾实例分割。在网络结构层面，首先在数据预处理环节对特征数据进行解耦，并通过双分支特征融合降低堆叠对遮挡物体特征的影响，从而解决复杂堆叠遮挡下的实例分割问题。针对该场景下的密集混淆问题，在候选框分类回归部分融入了级联分类器，并优化了分割网络分支的损失函数。实验采用堆叠垃圾分类实例分割数据集进行实验验证，实验结果表明，该方法的AP₅₀、平均准确率mAP等指标有较大提升，且具有较好的分割效果和一定的可解释性。

The garbage instance segmentation in complex stacked scenes is affected by serious occlusion and high density, which makes it more difficult to detect. To solve this problem, a case segmentation method combining DCT-Mask and the idea of double-layer feature fusion network were proposed, which is used for garbage case segmentation in highly stacked scenes. At the network structure level, firstly, the feature data was decoupled in the data pre-processing link, and the impact of stack on occluded object features was reduced through dual branch feature fusion, so as to solve the case segmentation problem under complex stack occlusion. To solve the problem of dense confusion in this scenario, a cascade classifier was incorporated into the candidate box classification regression part, and the loss function of dividing network branches was optimized. The experimental results show that the AP₅₀ , the average accuracy mAP and other indicators of this method have been greatly improved, and have a good segmentation effect and a certain degree of interpretability.