A Low-Light Object Detection Method Based on SAM-MSFF Network

The existing object detection methods are insufficient for low-light images due to their intrinsic property such as low contrast, detail loss and high noise. To solve this problem, a low-light object detection method that combines spatial-aware attention mechanism with multi-scale feature fusion (SAM-MSFF) is proposed. Firstly, multi-scale features are fused by multi-scale interactive memory pyramid to enhance effective information under low-illumination condition, and features of memory vector storage samples are set to capture potential correlation between samples. Then, a spatial-aware attention mechanism is introduced to obtain long-distance context information and local information of features in spatial domain, thereby enhancing the object features in low-light images and suppressing the interference of background information and noise. Finally, multiple receptive field enhancement module is used to expand receptive field of the features, and the features with different receptive fields are grouped and re-weighted, so that detection network can adaptively adjust the size of receptive field according to input multi-scale information. Experimental results on the ExDark dataset show that mAP (mean Average Precision) of the proposed method reaches 77.04%, which is 2.6%~14.34% higher than existing mainstream object detection methods. © 2024 Chinese Institute of Electronics. All rights reserved.