Improved Method for Oriented Waste Detection

Abstract

Waste detection is one of the main problems preventing the realization of automated waste classification, which is a basic function for robotic arms. In addition to object identification in general image analysis, a waste-sorting robotic arm not only needs to identify a target object but also needs to accurately judge its placement angle so that it can determine an appropriate angle for grasping. In order to solve the problem of low-accuracy image detection caused by irregular placement angles, in this work, we propose an improved oriented waste detection method based on YOLOv5. By optimizing the detection head of the YOLOv5 model, this method can generate an oriented detection box for a waste object that is placed at any angle. Based on the proposed scheme, we further improved three aspects of the performance of YOLOv5 in the detection of waste objects: the angular loss function was derived based on dynamic smoothing to enhance the model’s angular prediction ability, the backbone network was optimized with enhanced shallow features and attention mechanisms, and the feature aggregation network was improved to enhance the effects of feature multi-scale fusion. The experimental results showed that the detection performance of the proposed method for waste targets was better than other deep learning methods. Its average accuracy and recall were 93.9% and 94.8%, respectively, which were 11.6% and 7.6% higher than those of the original network, respectively.