Cross-Domain Adaptive Object Detection Based on CNN Image Enhancement in Foggy Conditions

This paper proposes a cross-domain adaptive object detection method based on CNN（convolutional neural networks）image enhancement, which addresses the problem that the accuracy of target detection algorithm decreases due to the low quality of images captured by the visual perception system of autonomous vehicles in foggy conditions. An end-to-end target detection network is constructed, which integrates DIP（digital image processing）and CNN adaptive image enhancement module, to improve the image quality in foggy weather through a small CNN parameter predictor that learns enhancement parameters adaptively. Furthermore, a multi-scale DA（domain adaptive）module is connected to YOLOv4 backbone, which through adversarial training, reduces the domain differences caused by foggy conditions and increases the accuracy of target detection in foggy weather. In the stage of training, CNN, DA and YOLOv4 are learned in an end-to-end manner. In the stage of detection, both CNN and DA modules are removed, only using the images that pre-training weights have adaptively detected in normal and foggy weather, which will not increase the complexity of the original network and thus satisfy the timing requirement of autonomous vehicles. An experiment based on the open dataset Foggy Cityscapes indicates that the proposed method can significantly enhance the image quality in foggy weather, increasing the average accuracy of target detection by 10.4%, which effectively enhances the target detection ability of autonomous vehicles in foggy conditions. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.