Driven by rapid advances in deep learning, object detection has been widely adopted across diverse application scenarios. However, in low-light conditions, critical visual cues of target objects are severely degraded, posing a significant challenge for accurate low-light object detection. Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images. For this purpose, this study proposes a DL-YOLO model specially tailored for low-light detection. To mitigate target feature attenuation introduced by repeated downsampling, we design a Multi-Scale Feature Convolution (MSF-Conv) module that captures rich, multi-level details via multi-scale feature learning, thereby reducing model complexity and computational cost. For feature fusion, we integrated the C3k2-DWR module by embedding the Dilation-wise Residual (DWR) mechanism into the 2-core optimized Cross Stage Partial (C3) framework, achieving efficient feature integration. In addition, we replace conventional localization losses with WIoU (Weighted Intersection over Union), which dynamically adjusts gradient gain according to sample quality, thereby improving localization robustness and precision. Experiments on the ExDark dataset demonstrate that DL-YOLO delivers strong low-light detection performance. The relevant code is published at https://github.com/cym0997/DL-YOLO.
Chang et al. (Thu,) studied this question.