LPDM-YOLOv8: A Lightweight Low-Light Object Detection Method Based on Laplacian Pyramid

Yahao Guo and Dongxiang Fu

European Journal of AI, Computing & Informatics, 2026, vol. 2, issue 1, 124-131

Abstract: To address the performance degradation of YOLOv8 under low-light conditions, this paper proposes a lightweight low-light object detection method based on a Laplacian pyramid. A Laplacian Pyramid Dimming Module (LPDM) is integrated into YOLOv8 to construct an end-to-end detection framework with illumination-adaptive enhancement and multi-scale feature fusion. The proposed module performs brightness-aware adjustment and hierarchical detail reconstruction, effectively improving feature representation in dark scenes with negligible computational overhead. Experiments on the ExDark dataset show that LPDM-YOLOv8n achieves an mAP of 0.524, corresponding to an 8.0% relative improvement over the baseline YOLOv8n, while maintaining real-time performance at 32 FPS. Notably, only 16 additional parameters are introduced without increasing FLOPs. The results demonstrate that the proposed method significantly enhances detection robustness under low-light conditions while preserving efficiency, making it suitable for real-time applications.

Keywords: YOLOv8; Laplacian pyramid; LPDM; brightness adaptation; multi-scale feature fusion; real-time object detection (search for similar items in EconPapers)
Date: 2026
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