Affiliations: [a] Fujian Key Laboratory of Green Intelligent Cleaning Technology and Equipment, School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, Fujian province, China
| [b]
Research and Development Department, Shunfeng Technology Co., Ltd., Shenzhen, Guangdong Province, China
Correspondence:
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Corresponding author. Li Wang, Research and Development Department, Shunfeng Technology Co., Ltd., Xuefu Road, Shenzhen, 518000, Guangdong Province, China. E-mail: [email protected].
Abstract: Unmanned sorting technology can significantly improve the transportation efficiency of the logistics industry, and package detection technology is an important component of unmanned sorting. This paper proposes a lightweight deep learning network called EPYOLO, in which a lightweight self-attention feature extraction backbone network named EPnet is also designed. It also reduces the Floating-Point Operations (FLOPs) and parameter count during the feature extraction process through an improved Contextual Transformer-slim (CoTs) self-attention module and GSNConv module. To balance network performance and obtain semantic information for express packages of different sizes and shapes, a multi-scale pyramid structure is adopted using the Feature Pyramid Network (FPN) and the Path Aggregation Network (PAN). Finally, comparative experiments were conducted with the state-of-the-art (SOTA) model by using a self-built dataset of express packages by using a self-built dataset of express packages, results demonstrate that the mean Average Precision (mAP) of the EPYOLO network reaches 98.8%, with parameter quantity only 11.63% of YOLOv8 s and FLOPs only 9.16% of YOLOv8 s. Moreover, compared to the YOLOv8 s network, the EPYOLO network shows superior detection performance for small targets and overlapping express packages.
Keywords: Object detection, express package detection, lightweight, deep learning
