Low-light object detection via adaptive enhancement and dynamic feature fusion

Under low-light conditions, object detection tasks face challenges such as low brightness, low contrast, and noise, which can lead to missed or incorrect detections. To address this issue, this paper proposes a low-light enhancement algorithm, called DAMFCN, and an improved DarkYOLOv8 method, aimed at enhancing low-light image quality and object detection performance. DAMFCN significantly improves the quality of low-light images by integrating the Low-Light Adaptive Module and the Multi-Scale Feature Compensation Block, where LLAM effectively extracts fine details and suppresses noise, and MSFCB compensates for lost details by integrating multi-scale information. The DarkYOLOv8 framework, built on the EfficientNet backbone, combines a multi-scale attention mechanism and the Dynamic Feature Fusion Attention Module, demonstrating superior object detection performance under low-light conditions. Experimental results show that the proposed methods outperform existing state-of-the-art techniques in terms of accuracy, robustness, and efficiency, offering broad application potential.