Railroad Catenary Insulator Fault Detection Based on Improved Faster
R-CNN
Authors: Yi Lingzhi , Dong Tengfei , Wang Yahui , She Haixiang , Yi Chuyang , Yu Guo 
Published: March 2024

Background:

The railroad catenary insulator, which is a crucial component of the catenary
system and is situated between the pillar and wrist arm, is crucial for electrical conductor isolation,
electrical equipment insulation, mechanical load bearing, anti-fouling, and anti-leakage. The
catenary insulators will experience tarnished flash, breakage, insulation strength deterioration, and
other issues as a result of the long-term outside unfavorable working circumstances. The train electrical
system's ability to operate normally is greatly hampered by these problems. Although there
are many patents and articles related to insulator fault detection, the precision is not high enough.
Therefore, it is crucial to improve the precision of catenary insulator fault detection.

Objective:

An improved region-based convolutional neural networks (Faster R-CNN)-based fault
detection method for railway catenary insulators is proposed in response to the long detection time
of the conventional railroad catenary insulator fault, the low precision of the catenary insulator
fault detection for occlusion and truncation, the poor performance of multi-scale object detection,
and the processing of class unbalance problem.

Methods:

The Faster R-CNN is optimized from four perspectives: feature extraction, feature fusion,
candidate box screening, and loss function, in accordance with the properties of the catenary
insulator. First, to solve the problem of multi-scale catenary insulator fault detection, convolutional
block attention module (CBAM) and feature pyramid network (FPN) are used to fuse the deep feature
and shallow features of the image. This results in a feature map with more critical semantic information
and higher resolution. After that, the weighted non-maximum suppression (WNMS) algorithm
improved by distance-intersection over union (DIOU) and Gaussian weighting function is
used instead of the traditional NMS algorithm, which effectively introduces the overlap of detection
frames into the confidence level and makes full use of the effective information of the detection
frames. Finally, the improved Focal loss is used as the classification loss, and the focusing parameter
and the balance factor of the Focal Loss are adjusted dynamically to solve the problem of
sample imbalance and difficult sample identification in the model better.

Results:

The effects of SSD, YOLOV3, traditional Faster R-CNN and improved Faster R-CNN
models are tested on the contact network insulator fault detection dataset constructed in this paper,
and the experimental results show that the improved Faster R-CNN has higher precision, recall,
and mAP compared to the other detection models, which reach 94.31%, 96.68% and 95.22%, respectively.

Conclusion:

The results of the experiments demonstrate that this method may successfully detect
the faults in different scale catenary insulators. It can effectively detect truncated, obscured faulty
catenary insulators. It has higher precision and recall and provides a reliable reference for maintaining
faulty insulators in railway catenary.