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 mAP@0.5:0.95 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.