GSC-YOLO: A Pedestrian Detection Method for Low-Light Security Surveillance Scenarios

Pedestrian detection in nighttime security surveillance and other low-light visual sensing tasks is an important foundation for intelligent perception in complex environments. Under low-light conditions, visible-light images often suffer from missing texture details, intensified noise, and reduced contrast, which can easily lead to insufficient target representation, unstable cross-scale feature fusion, and an increased risk of missed detections. Although multimodal schemes, such as RGB–infrared approaches, can improve detection performance by exploiting modal complementarity, they involve relatively high hardware costs, cross-modal calibration complexity, and system integration overhead, which impose deployment limitations in lightweight or cost-sensitive scenarios. Therefore, developing an efficient pedestrian detection method for low-light monocular RGB scenarios is of clear practical value. This study focuses on low-light monocular RGB pedestrian detection and proposes an application-oriented structurally optimized model, termed GSC-YOLO, built upon YOLOv13. First, GhostNetV3 is introduced as the backbone to enhance multi-scale feature representation under weak-texture conditions. Second, a Semantic–Spatial Alignment (SSA) module is designed to improve information compensation and suppress noise during the feature fusion stage. Finally, C2f_Faster is incorporated into the high-level semantic branch to optimize information flow and reduce redundant computation. On the RGB subsets of the two public datasets, LLVIP and KAIST, GSC-YOLO achieves [email protected]:0.95 values of 57.70% and 66.61%, respectively, and Recall values of 89.93% and 90.49%, respectively, consistently outperforming the YOLOv13 baseline. The results demonstrate that, under the experimental settings adopted in this study, the proposed method effectively improves pedestrian perception performance in low-light RGB scenes while maintaining favorable real-time inference capability, and may provide a useful reference for front-end vision sensing research in low-altitude intelligent networks.