Domain adaptation and knowledge distillation for lightweight pavement crack detection

Pavement crack detection is crucial for maintaining safe driving conditions; thus, the timely and accurate detection of cracks is of considerable importance. However, although deep neural networks (DNNs) have performed well in pavement crack detection, their dependence on large-scale labeled datasets, excessive model parameters, and high computational costs limit their application at the edge or on mobile devices. The conventional approaches concentrate on domain adaptation to leverage unlabeled data but overlook the domain shift issue, which can lead to performance degradation and is noticeable in lightweight models. Therefore, we propose a lightweight deep domain-adaptive crack detection network (L-DDACDN) to address these issues. Specifically, a novel distillation loss method that incorporates domain information, which facilitates the transfer of knowledge from a teacher model to a student model, is introduced. Additionally, L-DDACDN imitates the feature responses of a teacher model near the object anchor locations, ensuring that the student model effectively learns crucial features, thus addressing the domain shift issue and maintaining performance in lightweight models. Experimental results show that compared with the deep domain-adaptive crack detection network (DDACDN) trained with a large-scale pre-trained model, L-DDACDN has an average loss of only 3.5% and 3.9% in F1-scores and Accuracy, respectively. In contrast, the model parameters and FLOPs are reduced by approximately 92%. Additionally, compared to the YOLOv5, L-DDACDN demonstrates a notable improvement in the F1-scores and Accuracy on the CQU-BPDD dataset, revealing an average increase of 5% and 1.8% in F1-scores and Accuracy, respectively.