改进YOLOv3模型在无人机巡检输电线路部件缺陷检测中的应用研究

针对传统输电线路无人机巡检图像检测方法存在的精度低、计算时间长和训练样本少等问题，提出了一种用于输电线路部件绝缘子缺陷识别的改进YOLOv3模型。引入K-means++算法解决小目标不敏感问题，引入Focalloss函数解决样本不均衡问题，引入Mish激活函数提高模型精度，引入注意力机制Senet提高特征提取性能。通过对改进前后模型性能的比较分析，验证了该方法的优越性。结果表明，与传统的检测方法相比，所提方法在检测速度上能够满足实时检测的需要，且检测精度最优，检测时间为0.079 s,检测平均准确度均值为94.40%。该研究能够满足输电线路无人机巡检图像缺陷自动检测的需要。

Application of improved YOLOv3 model in defect detection of transmission line components in UAV patrol inspection

Aiming at the problems of low accuracy, long calculation time and few training samples in the traditional image detection method of UAV inspection of transmission line, an improved yolov3 model for insulator defect identification of transmission line components is proposed.Kmeans++ algorithm is introduced to solve the problem of small target insensitivity, focalloss function is introduced to solve the problem of sample imbalance, mish activation function is introduced to improve the accuracy of the model, and senet attention mechanism is introduced to improve the performance of feature extraction.Through the comparative analysis of the performance of the model before and after the improvement, the superiority of this method is verified.The results show that compared with the traditional detection methods, the proposed method can meet the needs of real-time detection in terms of detection speed, and the detection accuracy is the best, and the detection time is 0.079s, and the average detection accuracy is 94.40%. This research can meet the needs of automatic detection of image defects in transmission line UAV inspection.