基于改进YOLOv5的电子粉涂覆不均检测

放电管生产过程中,电极表面电子粉涂覆是否均匀是影响放电管产品质量的关键,目前主要依赖于人工目检,针对人工检测效率低、精度差等问题,提出一种基于改进YOLOv5的电子粉涂覆不均检测算法。首先,采集电极表面电子粉涂覆的图像制作数据集,并进行数据增强;其次,引入STDC模块优化主干特征提取网络,提高对难以辨认的金属电极表面缺陷不均的检测精度,并生成两个特征图以适应数据集;最后使用K-means++聚类优化自适应锚框计算。实验结果表明：改进YOLOv5算法对电子粉涂覆不均检测的mAP@50达到99.22%,与原YOLOv5网络相比提升了6.84%,大大提升了检测精度,相较于人工检测其效率更高。

Improved YOLOv5-based for detection of uneven coating of electronic powders

In the process of gas discharge tube production, the uniformity of electronic powder coating on the electrode surface is the key to the quality of gas discharge tube products, it is mainly detected by human eyes now. Aiming at the problems of low efficiency and poor accuracy of manual detection, an uneven electronic powder coating detection algorithm based on improved YOLOv5 is proposed. Firstly, the collected images of electron powder coating on the electrode surface are made into data sets, and data enhancement was performed. Secondly, the STDC module is used to optimize the backbone feature extraction network, to improve the detection accuracy of uneven surface defects of hard-to-recognize metal electrodes, and two feature layers are generated for adapting to the dataset size. Finally, Kmeans++ clustering is used to optimize the computation of adaptive anchor boxes. The experimental results show that the mAP@50 of the improved YOLOv5 algorithm proposed reaches 99.22%, which is 6.84% higher than that of the original YOLOv5 network, greatly improving the detection accuracy, and is more efficient than manual detection.