Adaptive Image Enhancement Method for Object Recognition Based on Statistical Photometric Characteristics
We propose an adaptive image enhancement method based on photometric statistics to improve object detection under adverse illumination conditions. Conventional image enhancement methods primarily target perceptual quality and may alter recognition-relevant features, potentially degrading detection performance. In contrast, the proposed method adaptively determines the enhancement intensity by combining a predefined domain-specific preset with a photometric risk score calculated from the photometric statistics of the input image to estimate the risk of enhancement-induced photometric risks. Based on these estimates, the enhancement intensity is adaptively controlled, and unnecessary transformation is conditionally bypassed. Experiments on the Berkeley DeepDrive 100K dataset using YOLOv11 demonstrate that the proposed method improves F1-score and mean Average Precision by 0.101 and 0.195, respectively, compared with unprocessed images under adverse conditions, including low-light environments. These results demonstrate that detector-oriented adaptive enhancement can improve robustness while reducing performance degradation caused by unnecessary or excessive image transformation.