As a significant innovation in modern transportation, autonomous driving technology offers unprecedented opportunities for improving traffic safety and travel convenience. However, under low-light conditions, insufficient illumination can cause object blurring, indistinct edges and confusion between the foreground and background, thereby compromising the reliability and safety of autonomous driving systems. Consequently, achieving robust object detection under low-light conditions has become a critical challenge in autonomous driving research. To address this issue, we propose DarkDet, a framework specifically designed for object detection in low-light environments. DarkDet consists of a Low-Light Enhancement Module (LLEM) and a Channel Fusion Module (CFM). The LLEM enhances features in dark regions of an image, while the CFM employs an attention mechanism to fuse multi-channel features, which are then fed into an object detector for final predictions. Furthermore, to expand object detection datasets tailored for low-light autonomous driving scenarios, we augment the original BDD100K dataset to construct Dark-BDD100K and collect an additional dataset, DarkCity, comprising 5,000 annotated nighttime urban road images. Finally, we evaluate the proposed DarkDet framework on Dark-BDD100K and DarkCity. Experimental results demonstrate that DarkDet achieves significant performance improvements under low-light conditions, effectively enhancing the accuracy and stability of object detection.
LI et al. (Wed,) studied this question.