This study proposes a lightweight detection algorithm, RABL-YOLOv8n, to address the problems of target blur, feature weakening, and small-scale target omission in pedestrian detection under low-illumination nighttime scenes. First, a lightweight RGCSPELAN module is designed, which significantly enhances the ability to capture small targets by optimizing the feature extraction process, while effectively reducing unnecessary computational and storage overhead. Second, attention for fine-grained classification (AFGC) mechanism is introduced in the 10th layer of the backbone network, which utilizes a multi-branch local perception strategy to enhance the recognizability of fine-grained features such as pedestrian clothing texture. Then, a bidirectional feature pyramid network (BiFPN) structure is adopted in the feature fusion layer, combined with an adaptive feature weighting strategy to further enhance the interaction capability of multi-scale features. Finally, the LSCD detection head is used to replace the original detection head. By decoupling the localization and classification tasks and introducing a lightweight context-aware module, the accuracy of small-object detection is significantly improved. The experimental results show that on the self-built NightPerson dataset, compared with the baseline YOLOv8n model, mAP@50 increases by 0.3%, precision decreases by only 0.013, recall increases by 0.009, while parameter count and floating point operations are reduced by 58% and 42%, respectively. Compared with YOLOv5n, YOLOv6n, YOLOv10n and other models, the proposed algorithm achieves the best balance between detection accuracy and model lightweighting.