Electric Power Research Institute, State Grid Henan Electric Power Company, Zhengzhou 450007, China.
Sensors (Basel). 2022 Oct 19;22(20):7961. doi: 10.3390/s22207961.
Rust of transmission line fittings is a major hidden risk to transmission safety. Since the fittings located at high altitude are inconvenient to detect and maintain, machine vision techniques have been introduced to realize the intelligent rust detection with the help of unmanned aerial vehicles (UAV). Due to the small size of fittings and disturbance of complex environmental background, however, there are often cases of missing detection and false detection. To improve the detection reliability and robustness, this paper proposes a new robust Faster R-CNN model with feature enhancement mechanism for the rust detection of transmission line fitting. Different from current methods that improve feature representation in front end, this paper adopts an idea of back-end feature enhancement. First, the residual network ResNet-101 is introduced as the backbone network to extract rich discriminative information from the UAV images. Second, a new feature enhancement mechanism is added after the region of interest (ROI) pooling layer. Through calculating the similarity between each region proposal and the others, the feature weights of the region proposals containing target object can be enhanced via the overlaying of the object's representation. The weight of the disturbance terms can then be relatively reduced. Empirical evaluation is conducted on some real-world UAV monitoring images. The comparative results demonstrate the effectiveness of the proposed model in terms of detection precision and recall rate, with the average precision of rust detection 97.07%, indicating that the proposed method can provide an reliable and robust solution for the rust detection.
输电线路金具生锈是输电安全的重大隐患。由于位于高空的金具不易检测和维护，因此引入机器视觉技术，借助无人机（UAV）实现智能生锈检测。然而，由于金具尺寸较小，以及复杂环境背景的干扰，常常存在漏检和误检的情况。为了提高检测的可靠性和鲁棒性，针对输电线路金具生锈检测问题，提出了一种具有特征增强机制的新型稳健 Faster R-CNN 模型。与当前在前端改进特征表示的方法不同，本文采用后端特征增强的思路。首先，引入残差网络 ResNet-101 作为骨干网络，从 UAV 图像中提取丰富的判别信息。其次，在感兴趣区域（ROI）池化层后添加新的特征增强机制。通过计算每个区域提议与其他区域提议之间的相似性，可以通过覆盖目标表示来增强包含目标对象的区域提议的特征权重，从而相对减少干扰项的权重。在一些真实的 UAV 监测图像上进行了实证评估。对比结果表明，所提出模型在检测精度和召回率方面具有有效性，生锈检测的平均精度为 97.07%，表明所提出的方法可以为生锈检测提供可靠和稳健的解决方案。
