YOLOv8-QR: An improved YOLOv8 model via attention mechanism for object detection of QR code defects.

Defect detection in Quick Response (QR) codes has important implications for downstream tasks. However, QR code defects include small objects and complex backgrounds, which makes their recognition effect poor. To address the above problems, we proposed a model based on YOLOv8, called YOLOv8-QR, to detect QR code defects. Specifically, first, the non-local attention (Non-local) module is introduced into the backbone of the YOLOv8 to enhance the interactive ability of the feature. The Non-local captures the correlation information of long-distance dependencies between features by calculating the attention weight between any positions. In addition, the contextual information required for representation learning of different defective objects is different. To extract multi-scale features, a Large Selective Kernel Network (LSKNet) was introduced. LSKNet dynamically adjusts the convolution receptive field of the neck fusion network and effectively uses the receptive field to capture the background information of different objects, thereby improving the representation ability of the model. To improve the defect detection accuracy of small objects in QR codes, the Normalized Gaussian Wasserstein Distance (NGWD) is introduced to replace the Intersection over Union (IoU) optimization function that is sensitive to the position deviation of objects and is not conducive to the regression of multi-scale objects. To verify the effectiveness of the model, the QR dataset was constructed and a series of experiments were conducted based on this dataset. The results show that the mAP 50 and mAP 50 : 95 of the YOLOv8-QR reach 95.5% and 65%, which are 3.8% and 2.3% higher than YOLOv8 respectively. The proposed YOLOv8-QR can better adapt to the needs of QR code defect detection in actual industrial environments. Our code is available at https://github.com/Code-of-Liujie/YOLOv8-QR.git. [ABSTRACT FROM AUTHOR]