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With the development of smart city in major cities at home and abroad, especially the management of smart city, how to improve the intelligence level of urban environment monitoring and evaluation has become an important research topic. It is of great value to rapidly and accurately detect garbage from urban images in the application of intelligent urban management. This paper aims to adopt a deep learning strategy for automatic garbage detection. By training a Faster R-CNN open source framework with region proposal network and ResNet network algorithm, we look over garbage detection results on garbage images. In addition, to improve the accuracy of the method, a data fusion and augmentation strategy is proposed. As a result, experiments show that the method has favorable generalization ability and high-precision detection function.