In low-light conditions, the detection scene can be harsh, some fundamental image features of the target to be lost, which can result in the disappearance of essential visual characteristics of the object to be detected. They have failed to balance the connection between the low-level semantic information of low-light images and normal images. This article proposes an algorithm for weak-supervised and adaptive object detection in the low-light environment for YOLOV7 (WSA-YOLO) that utilizes adaptive enhancement to effectively improve object detection capability in low-light environments, addressing this practical issue. The proposed decomposition network decomposes the image into reflectance and illumination maps, which are then enhanced separately. The proposed adaptive residual feature block (ARFB) effectively utilizes the feature correlation between low-light and normal-light images and shares the weights between them to improve parameter reuse capability during parameter prediction using the parameter prediction block. The proposed adaptive adjustment block and consistency loss function are used together to enhance the brightness and suppress noise. Finally, the you only look once (YOLO) framework is utilized for object classification, regression, and prediction. Using the metric mean average precision (mAP) for evaluation on the recognized datasets, the proposed WSA-YOLO has a performance improvement of about 8% in peak signal-to-noise ratio (PSNR), structural similarity index, and natural image quality evaluator (NIQE). And the increase in mAP is about 9%.
