The dropper clamp bolt is an important component of railway power supply line, which can affect the flow quality of electric locomotive. Therefore, this paper improves the SSD algorithm: Firstly, a lightweight neural network MobileNetV3 is introduced for front-end feature extraction to reduce the model complexity and improve the detection speed; secondly, CA attention mechanism to replace the SE module of the linear bottleneck layer with inverted residuals structure, aggregate the position information in the two directions of space, and the adjusted feature layer can capture the global remote feature information. Finally, the feature fusion module for reconstructing the feature layer is designed to adjust the small target detection layer to improve the detection effect of small targets. This paper also expands the training sample with CycleGAN to solve the problem of insufficient data set. The experimental results show that the model complexity of the improved algorithm decreased, and mAP @ 0.5 and FPS reached 95.5% and 81 fps, respectively. This study helps the transformation of catenary detection instruments to small mobile embedded devices.