A low light object detection method based on a modified YOLO11 model - Zhou, Meng; He, Shuke; Wang, Jing; Wang, Chang (Data Driven Control and Learning Systems Conference (Online), 2025)
In the field of computer vision, object detection in low-light scenarios remains an unresolved challenge. Traditional object-detection algorithms often encounter significant performance degradation under such conditions. This paper proposes a novel low light objective detection method based on a modified YOLO11 model. First, to improve the image quality in terms of brightness, contrast, and color fidelity, the self-calibrated illumination (SCI) module is utilized to enhance low-light images. Then, to enhance the model's ability to capture key object features, a CBAM-YOLO11 model is presented by integrating the convolutional block attention module (CBAM) into YOLO11 model, Finally, the proposed method is illustrated by EXdark dataset for low-light object detection. The results show that the proposed approach surpasses other state-of-the-art methods in detecting objects under low-light conditions. The mean average precision has been significantly improved, along with notable enhancements in recall and precision rates.