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.