Machine Vision-Based Intelligent Intrusion Detection Method for Obstacles on Open Railways in Low-Light Environments
Heng Zhou, Fengkui Chen, Xinyao Dong, Jikang Sun, Qing Yang, Dexin Gao · Applied Sciences · 2026
Railway obstacle detection in low-light environments faces challenges such as complex scenes and frequent obstacle intrusion across boundaries, and to address these issues, this paper proposes an improved RT-DETR-based method for low-light railway obstacle detection, named SWC-DETR. Firstly, the low-light image enhancement network SCINet is introduced to improve image quality in low-light environments and enhance the stability of feature extraction in the model; secondly, WTConv is integrated into the RepC3 module by combining wavelet transform with convolution to achieve a balance between a large receptive field and low parameter count; and thirdly, the CloFormer dual-branch structure is incorporated into the AIFI module to further suppress background noise under low-light conditions and strengthen the representation of edge features for small targets. In this paper, a low-light open railway obstacle detection dataset is constructed, and extensive comparative experiments along with multiple independent runs are conducted. The results demonstrate that the improved model reduces the number of parameters and the computational complexity by 18.1% and 33.8%, respectively, while consistently achieving a [email protected] of 72.2% and a recall of 66.6% (representing an average improvement of 5.9% and 5.5% over the original model, respectively), achieving model lightweighting while significantly improving the accuracy of obstacle detection in low-light environments and providing effective technical support for the safety protection of open railways.