![]() It is significantly important to detect and deal with the rust of transmission line fitting in a timely manner. 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.ĭue to long-term exposure to the wild environment, transmission line fittings are prone to defects such as aging, damage and rust, resulting in heavy risk to the transmission safety. Empirical evaluation is conducted on some real-world UAV monitoring images. The weight of the disturbance terms can then be relatively reduced. 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. Second, a new feature enhancement mechanism is added after the region of interest (ROI) pooling layer. First, the residual network ResNet-101 is introduced as the backbone network to extract rich discriminative information from the UAV images. ![]() Different from current methods that improve feature representation in front end, this paper adopts an idea of back-end feature enhancement. 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. Due to the small size of fittings and disturbance of complex environmental background, however, there are often cases of missing detection and false detection. 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). Rust of transmission line fittings is a major hidden risk to transmission safety.
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