Regional Feature Enhancement Domain Adaptation for Automatic Road Damage Detection in Hazy and Rainy Weather

Currently, most neural networks for automatic road damage detection are trained using labeled normal weather datasets, and the detection performance decreases dramatically in hazy and rainy weather, while labeling adverse weather road damage datasets is a rather difficult task. Therefore, we propose a regional feature enhancement domain adaptation method (RFEDA), which employs a two-stage strategy of inter-domain adaptation and intra-domain adaptation to alleviate the domain shift between normal weather and hazy and rainy weather road damage images. Specifically, RFEDA uses a regional feature enhancement module (RFEM) to segment the damage region. The semantic segmentation task facilitates the object detection task to locate the damage object more accurately through path sharing, thus suppressing the interference of background noise. Meanwhile, the segmented region features are utilized to construct instance-level features for instance-level feature alignment. In addition, the multi-scale features extracted by backbone are used for image-level feature alignment. In the intra-domain adaptation stage, the model after inter-domain adaptation is used to generate high-confidence pseudo-labels for the training set of the target domain, and the pseudo-labels are used for self-training of the model and thus for fine-tuning the model. Extensive experiments on the CNRDD and Japan-RDD datasets in hazy and rainy weather demonstrate the effectiveness of our method. Compared with no domain adaptation, RFEDA can improve the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i>@0.5 by up to 16.9% and 7.4% in hazy and rainy weather, respectively.