MGL-YOLO: A Lightweight Barcode Target Detection Algorithm

Due to the critical importance of one-dimensional barcode detection in logistics, retail, and manufacturing, which has become a key issue affecting operational efficiency, researchers have shown increasing interest in this area. However, deploying deep convolutional neural networks on embedded and some edge devices is very challenging due to limited storage space and computational resources. To address this issue, this paper proposes MGL-YOLO, a lightweight one-dimensional barcode detection network based on an improved YOLOv8, which aims to achieve a high detection accuracy at low computational cost. First, a new multi-scale group convolution (MSGConv) is designed and integrated into the C2f module to construct the MSG-C2f feature extraction module. By replacing the C2f module in the P5 layer of the backbone network, the ability to extract multi-scale feature information is enhanced. Secondly, a feature extraction module, Group RepConv Cross Stage Partial Efficient Long-Range Attention Network (GRCE), is designed to optimize the feature extraction capability of the C2f modules in the neck section, offering significant advantages in multi-scale characteristics and complexity adjustment. Finally, a Lightweight Shared Multi-Scale Detection Head (LSMD) is proposed, which improves the model's detection accuracy and adaptability while reducing the model's parameter size and computational complexity. Experimental results show that the proposed algorithm increases MAP50 and MAP50.95 by 2.57% and 2.31%, respectively, compared to YOLOv8, while reducing parameter size and computational cost by 36.21% and 34.15%, respectively. Moreover, it also demonstrates advantages in average precision compared to other object detection networks, proving the effectiveness of MGL-YOLO for one-dimensional barcode detection in complex backgrounds.