YOLOv11n-LSL: An Efficient Network for Foreign Object Intrusion Detection in Complex Railway Environments

Safe rail transit operation is essential for socioeconomic development and the protection of public life and property. Traditional railway foreign object intrusion detection methods suffer from insufficient detection accuracy and poor real-time performance under complex scene conditions. While deep learning has achieved remarkable performance in general object detection tasks, existing lightweight detectors still face prominent challenges in railway scenarios, including severe background clutter, drastic variations in target scales, and constrained edge computing resources. To tackle the above issues, this paper proposes YOLOv11n-LSL, an improved lightweight and high-precision detector based on YOLOv11n. Specifically, a C2PSA-SWSA shifted-window self-attention module is designed to suppress background interference and improve the feature representation of small targets; an SPPF-LSKA large-kernel attention module is introduced to construct a large and adaptive receptive field, thereby improving the detection capability for foreign objects of different scales; in addition, the lightweight adaptive decoupled head (LADH) is introduced to alleviate feature conflicts between the classification and regression branches and reduce network parameter redundancy. Comparative experiments on a self-built railway foreign object intrusion dataset show that the proposed YOLOv11n-LSL outperforms the original YOLOv11n, achieving 84.6% [email protected], 88.1% precision, and 79.5% recall, which are 3.0, 6.0, and 7.8 percentage points higher than the baseline, respectively. The model only contains 2.522 M parameters and achieves a single-frame latency of 12.3 ms. Compared with the original YOLOv11n with 2.583 M parameters, the parameter count is reduced by 2.4%, and the inference latency is decreased by 41.1%. The experimental results show that the proposed method effectively improves detection accuracy in complex railway scenes while maintaining lightweight inference performance.