Chen Zhikang, Xiao Yao, Zhou Qi, Li Yudong, Chen Bin
Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China.
Xi'an Shaangu Power Co., Ltd., Xi'an, 710075, China.
Environ Monit Assess. 2024 Dec 26;197(1):103. doi: 10.1007/s10661-024-13595-x.
Waste sorting is a key part of sustainable development. To maximize the recovery of resources and reduce labor costs, a waste management and classification system is established. In the system, we use Internet of Things (IoT) and edge computing to implement waste sorting and the systematic long-distance information transmission and monitoring. A dataset of recyclable waste images with realistic backgrounds was collected, where the images contained multiple waste categories in a single image. An improved deep learning model based on YOLOv7-tiny is proposed to adapt to the realistic complex background of waste images. In the model, adding partial convolution (PConv) to Efficient Layer Aggregation Network (ELAN) module reduces parameters and floating point of operations (FLOPs). Coordinate attention (CA) is added to spatial pyramid pooling (Sppcspc) module and ELAN module, respectively. SIoU loss function is used, which improves the recognition accuracy of the model. The improved model shows a higher accuracy on the basis of lighter weight and is more suitable for deployment on edge devices. The proposed model and the original model were trained using our dataset, and their performance was compared. According to the experimental results, mAP@.5, mAP@.5:.95 of the improved YOLOv7-tiny are increased by 1.7% and 1.4%, and the parameter and FLOPs are decreased by 4.8% and 5%, respectively. The improved model has an average inference time of 110 ms and an FPS of 9 on the Jetson Nano. Hence, we believe that the developed system can better meet the needs of current garbage collection system.
