In response to the difficulty in detecting defects in high-speed rail clip springs caused by complex lighting environments, an improved Faster Region Convolutional Neural Networks (R-CNN)-based defect detection method for clip springs is proposed. By extracting defect feature maps through multi-layer convolutional neural networks, the network's attention to defect features is enhanced, and the impact of interference from complex lighting environments is reduced. A region proposal network is designed to generate candidate regions, and based on these regions, pooling is performed to extract the corresponding specific defect locations in the feature maps. The fully connected layers of the region proposal network are employed to calculate the specific categories and precise locations of defects, yielding the final detection results. The proposed algorithm can fully suppress the interference of lighting environments, significantly enhance the representation ability of defect features, simplify the image pre-processing stage, and reduce the requirements for the quality of the original image. Experimental results show that the proposed algorithm can effectively detect defects in high-speed rail clip springs, and compared to existing algorithms, it has a higher accuracy, stronger robustness, and significantly improved computational efficiency.