针对输电线上穿刺线夹及螺栓易受光照、遮挡、环境背景、拍摄角度等因素影响,提出了一种基于改进Faster R-CNN的检测方法。对获取的数据采用翻转、平移、角度旋转等方式增强数据集;再对比不同数量训练集对模型的影响;由于螺栓体积很小,使用网络深度更深、运算量更小的深度残差网络(ResNet50)代替VGG-16(Visual Geometry Group 16)网络并对图像进行特征提取;分析不同模型和参数对识别精确度的影响。结果表明,改进Faster R-CNN模型的mAP值达到92.4%,与未改进的Faster R-CNN模型相比提高2.8个百分点。利用深度学习目标检测模型能更好地检测不同分辨率和不同位置角度的穿刺线夹及螺栓,该模型具有较高的工程实用价值。

In this study, we propose a method based on improved Faster R-CNN with respect to the influence of light penetration, occlusion, environmental background, and shooting angle on the insulation piercing connectors and bolts on the transmission line. First, we enhanced the acquired datasets via flipping, panning, and angle rotation. Second, we compared the influences of different training sets on the model. Finally, we used a deep residual network (ResNet50) having a considerable network depth and less amount of computation to replace the VGG-16 (Visual Geometry Group 16) network for extracting the image features owing to the small size of the bolt. Further, we analyzed the influences of different models and parameters on the identification accuracy. The result proves that the improved Faster R-CNN model has an mAP value of 92.4%, which is 2.8 percentage higher than that of the unmodified Faster R-CNN model. The deep learning target detection model can be used to appropriately detect and identify the insulation piercing connectors as well as bolts having different resolutions and position angles. Therefore, this model has a high engineering application value.