C2T-HR3D: Cross-Fusion of CNN and Transformer for High-Speed Railway Dropper Defect Detection

The dropper plays a critical role in the overhead contact system (OCS) of high-speed railways, ensuring smooth power transmission and reducing vibration between the contact and messenger wires. However, adverse factors, such as temperature variations, inclement weather, and high-frequency vibrations can lead to dropper loosening and detachment, which deteriorates the collecting current through the pantograph. In severe cases, it can even result in pantograph breakage or contact wire damage, ultimately causing train malfunctions. Unfortunately, existing detection methods fall short in recognizing dropper defects in real-world scenarios. To address this challenge, we propose a novel cross-fusion of convolutional neural network and transformer for high-speed railway dropper defect detection (C2T-HR3D) network. Leveraging a cross-fusion of convolutional neural network (CNN) and transformers, this network accurately recognizes dropper defects in challenging scenarios, such as fog, rain, sun, and night-time conditions. Moreover, it can also accurately identify obscured and small dropper defects from a long distance, significantly improving recall and precision. Extensive experiments have demonstrated that our network outperforms CNN-based, transformer-based, and CNN-transformer state-of-the-art networks by 3.4%, 1.8%, and 2.1%, respectively. The C2T-HR3D network has been successfully deployed on over 300 high-speed trains, detecting more than 10000 dropper defects.