无线电工程2024，Vol.54Issue(6)：1431-1439,9.DOI:10.3969/j.issn.1003-3106.2024.06.011
基于DMSANet-YOLOv7的雾霾下绝缘子缺陷实时检测方法
Real-time Detection of Insulator Defects Under Haze Weather Based on DMSANet-YOLOv7
摘要
Abstract
Aiming at the situation that the insulator defect is too small in complex environment and haze weather and the traditional target detection algorithm is difficult to identify,resulting in false and missing detection,an improved defect detection algorithm based on YOLOv7 model is proposed.In the image preprocessing part,dark channel prior defogging algorithm is used to improve the feature resolution and robustness of the model.In order to improve the ability of feature extraction and small target recognition,Dual Multi Scale Attention Network(DMSANet)mechanism is introduced into the back end of the backbone network structure.In order to reduce the model size and improve the model recognition speed,the improved C3 module based on SwinTransformer is used to replace the E-ELAN module.The Wise-IOU loss function is used in the prediction part to improve the convergence efficiency of the model.The experimental results show that compared with the original YOLOv7 algorithm,DMSANet-YOLOv7 algorithm has improved mAP,accuracy and recall rate by 6.3％,7.9％and 12.3％,respectively.The detection speed of a single image reaches 12.3 ms,and the number of parameters is 37.7 M.While improving the detection accuracy,it can ensure the balance of detection speed and performance,and can be better mounted on drones and other platforms to meet the real-time dynamic detection requirements of insulators and their defects.关键词
绝缘子缺陷/目标检测/注意力机制/YOLOv7/暗通道先验去雾算法Key words
insulator defects/target detection/attention mechanism/YOLOv7/dark channel prior defogging algorithm分类
信息技术与安全科学引用本文复制引用
王海群,王康..基于DMSANet-YOLOv7的雾霾下绝缘子缺陷实时检测方法[J].无线电工程,2024,54(6):1431-1439,9.基金项目
河北省自然科学基金(F2021209006)Hebei Provincial Natural Science Foundation of China(F2021209006) （F2021209006）
