【摘要】 针对印刷生产中传统人工缺陷检测耗时耗力、小缺陷区域不易检测，以及传统图像处理方法鲁棒性差等问题，提出一种基于改进YOLOv5l模型的印刷品缺陷检测算法。首先，通过增加浅层特征图拓展检测尺度，以捕获微小缺陷信息，从而提高网络对小目标的检测能力；然后，使用全维度动态卷积替换Neck区中的普通卷积，以增强网络对印刷缺陷上下文信息的捕获能力；最后，为解决前两项工作带来的检测速度下降的问题，采用C3Ghost替换Neck中的C3模块，在检测精度损耗极低的情况下尽可能地提高检测速度。实验结果表明：改进后的YOLOv5l算法的平均精度均值（mAP）达到97.3%，较原YOLOv5l算法和现有的印刷品缺陷检测算法Siamese-YOLOv4的精度分别提高2.9百分点和2.7百分点；检测速度为44.1 frame/s。所提算法对印刷品缺陷的分类和定位效果优于原YOLOv5l和Siamese-YOLOv4算法，具有较高的检测精度和检测速度，可以应用于印刷质检来提高生产质量管控水平、降低人工成本。更多还原

【Abstract】 Herein, to address the issues associated with traditional manual defect detection in printing production, such as time and effort consumption, difficulty in detecting small defect areas, and poor robustness, an improved YOLOv5l-based printing defect detection algorithm is proposed. First, by expanding the detection scale by adding shallow feature maps to capture small defect information, the ability of the network to detect small targets is improved. Subsequently, the ordinary convolution in the Neck area is replaced with full-dimensional dynamic convolution to enhance the ability of the network to capture contextual printing defect information. Finally, to address the issue of reduced detection speed caused by the aforementioned two modifications, the C3 module in the Neck area is replaced with C3Ghost to improve detection speed to the maximum extent possible with considerably low detection accuracy loss. Experimental results show that the proposed algorithm has a detection speed of 44. 1 frame/s, and its mean average precision(mAP) reaches 97. 3%, which is 2. 9percentage points and 2. 7 percentage points higher than those of the original YOLOv5l algorithm and an existing printing defect detection algorithm-Siamese-YOLOv4, respectively. The proposed algorithm outperforms the original YOLOv5l and Siamese-YOLOv4 algorithms in classifying and locating defects in printed products with high detection accuracy and speed. Thus, the proposed algorithm can be applied to print quality inspection to improve production quality control levels and reduce labor costs.更多还原