Attention-Guided Low-Light Object Detection Based on Receptive Field With Adaptive Depth Convolution

To solve the performance degradation of visible light-based object detectors affected by low-light environments, an attention-guided detection model based on adaptive depthwise convolution is proposed. First, the quality of the original image is improved, and various image information is restored by an image enhancement module named zero-reference dual-illumination deep-curve estimation++ (Zero-DiDCE++). Then, the article further introduces a self-attention mechanism and receptive field features, designing the feature self-attention (FSA) module and the sparse receptive field adaptive depth convolution (SRDConv) module. These modules work in concert to accentuate target features and improve detection accuracy. Finally, we introduce a framework named ZRF-you only look once (YOLO)v9 for object detection, which is designed to improve performance in low-light conditions. It incorporates Zero-DiDCE++ and connects it with an improved YOLOv9 network for algorithm integration. Experiments show that based on the ExDark, Dark Face, and Tiny-Person datasets, the ZRF-YOLOv9 algorithm improves the low-light detection accuracy by 1.1%, 2.0%, and 0.5% in mAP ${}_{5}{}_{0}$ . This model not only enhances the visibility of the image but also improves detection accuracy for target images in low-light scenes.