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With the development of social economy and the continuous improvement of people"s living standard, the production of garbage has climbed dramatically. In order to effectively deal with the problems of low efficiency and poor accuracy of garbage sorting, a garbage detection algorithm based on YOLOv7 network as a base model is proposed.The algorithm carried out a series of modifications to the YOLOv7 network, firstly, the attention mechanism SimAM was added to the head module, which enhanced the model"s perceptual ability and adaptive ability so as to improve the detection accuracy; furtherly, non-maximum suppression (soft-NMS) was replaced in the backbone network to remove redundant detection frames while the loss function was improved to be the edge regression loss function SIoU, which revitalized the accuracy and speed of detection; finally, the C3 module was utilized to replace the ELAN-W module that YOLOv7 could promote the network"s ability to detect smaller targets. The proposed network was tested by the data-set, and the average accuracy is 98.93% and the training time is 27.58h, which is better than the original model"s 96.31% and 44.53h. The experimental results show that the improved algorithm has a more obvious enhancement in detection.