A low illumination target detection method based on a dynamic gradient gain allocation strategy

Current target detection methods perform well under normal lighting conditions; however, they encounter challenges in effectively extracting features, leading to false detections and missed detections in low illumination environments. To address these issues, this study introduces an efficient target detection method for low illumination, named DimNet. This method optimizes the model through enhancements in multi-scale feature fusion, feature extraction, detection head, and loss function. Firstly, efficient multi-scale feature fusion is performed by using a new neck structure in the original model so that it can fully exchange high-level semantic information and low-level spatial information. Secondly, by designing a new feature aggregation module, it can simultaneously fuse channel and spatial information as well as local and global information to improve the representation of the network. Subsequently, to achieve more accurate target recognition, a new detection head is designed by replacing the original convolutional layer and utilizing the reparameterization technique, which enhances recognition performance in complex scenes. Additionally, the size of the improved detection head is reduced by adopting a parameter-sharing approach, thereby balancing detection accuracy with computational efficiency. Finally, to solve the fuzzy boundary problem caused by the target boundary being similar to the surrounding background due to insufficient illumination under low illumination conditions, a new loss function is designed in this paper, which pays more attention to the center of the target and weakly considers the aspect ratio of the target prediction frame, and at the same time, the new loss function employs a dynamic gradient gain assignment strategy to reduce the effect of the low-quality anchor frames and to improve the target localization Accuracy. The experimental results show that DimNet achieves a mAP₅₀ of 75.60% on the ExDark dataset, which is an improvement of 3.77% over the baseline model and 2.25% over the state-of-the-art (SOTA) model. DimNet outperforms the previous and current SOTA methods in terms of detection accuracy and other aspects of performance, which is a clear advantage.