A garbage sorting method using an adaptive deep neural network

Achieving automatic sorting of garbage through a mechanical arm depends on accurate recognition and localization of garbage. In this paper, we propose a garbage sorting method based on an adaptive deep neural network. The method addresses the limitations of YOLOv5 object detection algorithm, such as the fixed number of anchor boxes and the inability of the feature fusion network to adjust according to the target scale. Our proposed method introduces an object detection algorithm based on an adaptive deep neural network. We use the adaptive K-means clustering algorithm to automatically determine the initial clustering center and the number of clusters, extract features of multiple scales using the feature extraction backbone network, and automatically adjust the structure and feature fusion times of the adaptive feature fusion network based on the clustering results of adaptive K-means. We test the proposed algorithm and YOLOv5 object detection algorithm on a self-made garbage classification dataset. The experiments demonstrate that our proposed adaptive deep neural network reduces the model parameters of YOLOv5 by 27.03%, improves the detection speed by 18%, and enhances the detection accuracy by 0.7%. Finally, we transplant the adaptive deep neural network to the garbage sorting platform and use it for real-time garbage sorting.