LADF-YOLO: A Highly Accurate Low-Light Target Detection Algorithm

Images captured in complex low-light environments often exhibit weak contrast, high noise, and blurred edges. Directly applying existing target detection models to low-light images can lead to missing details and inaccurate localization, resulting in poor detection accuracy. To address these issues, this paper presents a low-light target detection method based on LADF-YOLO. The method first introduces a ReS Feature Pyramid Network ReSFPN integrated with a backbone network to capture more effective image features in low-light conditions. The method then designs a detection head that eliminates the need for non-maximum suppression NMS-Free , utilizing a dual-label assignment strategy and a consistent matching metric to align the optimization direction of the head, thereby enhancing the model's overall performance. Finally, experiments on the real low-light image dataset DarkFace demonstrate that the proposed LADF-YOLO outperforms other leading target detection algorithms in low-light conditions. Compared to the benchmark model YOLOv8, LADF-YOLO achieves a 10.8 improvement in mAP@0.5 and a 9.9 improvement in Recall.