Improved YOLOv8s-Based Spray Code Detection Algorithm

The traditional method of code detection relies on manually extracting barcodes and QR codes from paper, which is a cumbersome process and prone to human error, leading to low detection efficiency and insufficient accuracy. To address these issues, this paper proposes an improved YOLOv8s-based code detection algorithm that incorporates the LSK attention mechanism and the WIoU loss function, enabling automatic recognition and localization of barcodes and QR codes on printing devices. Compared to traditional methods, this algorithm significantly improves detection efficiency and accuracy, reduces manual intervention, and optimizes the debugging process. Experimental results show that the detection accuracy of the algorithm exceeds 0.81 and is capable of maintaining efficient and accurate detection performance in complex backgrounds and diverse printing environments.