Advanced Object Detection in Low-Light Conditions: Enhancements to YOLOv7 Framework
Abstract
Object detection in low-light conditions is increasingly relevant across various applications, presenting a challenge for improving accuracy. This study employs the popular YOLOv7 framework and examines low-light image characteristics, implementing performance enhancement strategies tailored to these conditions. We integrate an agile hybrid convolutional module to enhance edge information extraction, improving detailed discernment in low-light scenes. Convolutional attention and deformable convolutional modules are added to extract rich semantic information. Cross-layer connection structures are established to reinforce critical information, enhancing feature representation. We use brightness-adjusted data augmentation and a novel bounding box loss function to improve detection performance. Evaluations on the ExDark dataset show that our method achieved an mAP50 of 80.1% and an mAP50:95 of 52.3%, improving by 8.6% and 11.5% over the baseline model, respectively. These results validate the effectiveness of our approach for low-light object detection.
