Garbage recycling offers many benefits, e.g., protection of water and soil resources, quality improvement of the living environment of residents, and accelerated development of green circular economy. However, traditional garbage recycling methods incur excessive labor and resource costs. In this work, we propose a lighter YOLOv5s improved model in which ShuffleNet v2 and deep separable convolution methods are combined to better solve the problems in garbage recycling by classifying and locating recyclable garbage more efficiently. Experimental results show that number of parameters of the improved model is only 38.98% of that of the original model. When the input resolution is 640 × 640, the mean average precision (mAP) of the improved model is 94.01%, which is 1.91 percentage points higher than the original YOLOv5s. With regard to the computing speed, the forward propagation time of the improved model is 11.5% greater than that of the original YOLOv5s by deploying on hardware of Jetson Nano. Moreover, compared with the current mainstream target detection models, the improved model has a good ability to express the characteristics of recyclable garbage.