In In the field of garbage intelligent identification, similar garbage are difficult to be effectively detected due to different kinds of characteristics. This paper proposes a Skip-YOLO model for garbage detection in real life through the visual analysis of feature mapping in different neural networks. First of all, the receptive field of the model is enlarged through the large-size convolution kernel, which enhanced the shallow information of images. Secondly, the high-dimensional feature mappings of garbage is extracted by dense convolutional blocks. The sensitivity of similar features in the same type of garbage is enhanced by strengthening the sharing of shallow low semantics and deep high semantics information. Finally, the multi-scale high-dimensional feature mappings is integrated and sent to the YOLO layer to predict the type and location of garbage. Experimental results show that compared with the YOLOv3, the overall detection precision is increased by 22.5%, and the average recall rate is increased by 18.6%. In qualitative comparison, it successfully detects domestic garbage in complex multi-scenes. In addition, our approach alleviates the overfitting problem of deep residual blocks. The application case of waste sorting production line is used to further highlight the model generalization performance of our method.