LLD-YOLO: A Low-Light Object Detection Algorithm Based on Dynamic Weighted Fusion of Shallow and Deep Features

Object detection in low-light environments has widespread applications, but existing low-light detection algorithms often suffer from issues such as the loss of scarce low-level features in low-light images, poor performance in detecting multi-scale objects, and reduced accuracy in locating objects with blurred edges or occlusions, resulting in suboptimal performance in low-light scenarios. To address these challenges, we propose a novel feature fusion structure, SRB-FPN, and optimize the regression loss function, integrating it with the YOLOv8 model to create a detector, LLD-YOLO, specifically designed for low-light conditions. To emphasize the key unit information in feature maps, while dynamically adjusting the fusion ratio between shallow and deep features based on their importance, we introduce the SDFBF module to preserve crucial low-level semantics.To enhance the diversity of receptive fields during training, enabling the capture of multi-scale features, while avoiding additional inference overhead, we incorporate the DBB-c2f module.To make the model focus more on challenging samples in low-light conditions and more accurately capture the differences in bounding box boundaries, we combine Focaler IoU and MPDIoU, and propose the Focal MPDIoU Loss. Finally, the proposed model achieves a mAP50 of 70.0% on the ExDark dataset, which is a 2.7% improvement over the baseline. Extensive experiments on the ExDark, NOD, and RTTS datasets demonstrate that LLD-YOLO exhibits exceptional detection performance in low-light environments and strong adaptability in foggy weather scenarios.