DuoEnhance: An Object Detection Method With Adaptive Image Enhancement and Feature Enhancement in Foggy Weather

ABSTRACT

Although significant progress has been made in object detection, detecting objects in foggy conditions remains a challenging task. Fog reduces image clarity, thereby affecting the detection performance of the model. In order to address this challenge, we enhance the model's detection performance in foggy scenes through a task‐driven adaptive image enhancement method and a feature enhancement strategy (DuoEnhance). We first analyse image enhancement techniques and propose an adaptive image enhancement method for dehazing foggy images, leveraging physical priors and gamma correction. In addition, we introduce dilated‐aware weighted convolution, which enhances the model's feature extraction capability through a dynamic multi‐scale feature weighting strategy. To validate the generality and effectiveness of the proposed method, we further conduct extensive experiments on both YOLO‐based and DETR‐based detection frameworks. Due to the complexity of objects in foggy weather, we not only validate the effectiveness of the model on the synthetic dataset foggy cityscapes, but also validate model generalization on the real‐world task‐driven testing set. The experimental results show that the model is able to significantly improve mean average precision (mAP), mAP@0.5 and recall ( R ) with low floating point operations (FLOPs) on all two datasets, reducing the risk of missed or false object detections. Notably, the small model achieves a 1.5% improvement in mAP and a 2.5% increase in R , with only 24.6G FLOPs on the foggy cityscapes dataset.