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The rapid growth in the output and types of plastic products has brought great challenges to the recycling of waste and miscellaneous plastics. At present, it still relies on a large number of manual sorting, facing the harsh and high-intensity working environment, it is undoubtedly urgent to upgrade automation. To solve the above problems, an improved FoveaBox target detection algorithm is proposed. In view of the complicated background of waste plastic sorting, ResNeXt-101 is used as the backbone network to replace ResNet-50 to improve the feature extraction ability. Aiming at the problem of large shape differences, a deformable convolution with a zoom factor is used to improve the effective receptive field of the convolution process. Aiming at the problem of mutual occlusion between targets, a softened weighted anchor point mechanism with hierarchical control factors is used to improve the detection accuracy of close targets. The results show that the mean average precision of the waste plastic detection algorithm based on the improved FoveaBox reaches 85.79%, and the detection speed is 71.4ms, which has strong practicability.