A Lightweight Deep Learning Algorithm for Multi-Objective Detection of Recyclable Domestic Waste

In light of the rapid development of human society, there has been a notable surge in waste production, which has resulted in environmental pollution and degradation. This is a pervasive issue that requires attention. To address the environmental problems caused by waste generation and advance the development of recyclable domestic waste detection, this article proposes waste classification as a solution. Traditional waste sorting methods have proven to be inefficient and prone to errors, hence the need for a more effective approach. A multiobjective recyclable domestic waste detection and classification method based on improved You Only Look Once v5s (YOLOv5s) is proposed in this study. In this study, the network structure is enhanced through the implementation of the Bidirectional Pyramid Network (BiFPN). The coordinate attention mechanism is then incorporated to elevate the accuracy of the model. Additionally, the loss function is refined by adopting the Efficient Intersection Over Union Loss (EIOU_Loss) metric to further optimize network performance. Finally, the introduction of the Ghost convolution module reduces parameter count and significantly improves the real-time detection speed. The waste dataset named Multi-classified Recyclable Domestic Trash Identification Dataset (MULTI-TRASH), which is composed of machine shooting, web crawler, and artificial photography, is used for verification due to its good generalization and representativeness. The mean Average Precision at a threshold of 0.5 ([email protected]) value of 94.8% is achieved by the improved model, which is a 30.72% reduction in the number of parameters and a 1.2% improvement in the [email protected] value compared with YOLOv5s. The effectiveness of the proposed algorithm is proved by a comparison with other target detection algorithms. This study aims to provide technical references for the development of a recyclable domestic waste detection system.