An Efficient Printing Defect Detection Based on YOLOv5-DCN-LSK

During the production process of inkjet printing labels, printing defects can occur, affecting the readability of product information. The distinctive shapes and subtlety of printing defects present a significant challenge for achieving high accuracy and rapid detection in existing deep learning-based defect detection systems. To overcome this problem, we propose an improved model based on the structure of the YOLOv5 network to enhance the detection performance of printing defects. The main improvements include the following: First, we introduce the C3-DCN module to replace the C3 module in the backbone network, enhancing the model's ability to detect narrow and elongated defects. Secondly, we incorporate the Large Selective Kernel (LSK) and RepConv modules into the feature fusion network, while also integrating a loss function that combines Normalized Gaussian Wasserstein Distance (NWD) with Efficient IoU (EIoU) to enhance the model's focus on small targets. Finally, we apply model pruning techniques to reduce the model's size and parameter count, thereby achieving faster detection. Experimental results demonstrate that the improved YOLOv5 achieved a [email protected] of 0.741 after training, with 323.2 FPS, which is 2.7 and 20.8% higher than that of YOLOv5, respectively. The method meets the requirements of high precision and high efficiency for printing defect detection.