YOLOv8-CDD: an improved concrete defect detection method combined CNN with transformer

Abstract

Concrete defect detection is a critical task to ensure the safe and stable operation of concrete infrastructure. In order to effectively solve the problems of low efficiency, high cost and poor performance of existing methods, a high-precision concrete defect detection method YOLOv8-CDD(Concrete Defect Detection) combining Convolutional Neural Network(CNN) and Transformer is proposed in this paper. Firstly, based on the features with a large span of concrete defect features, a Bot-Transformer(BoT) module that can effectively extract the global information of defect features is proposed to improve the network's ability to extract global features. Secondly, in order to further strengthen the interaction between defect feature channels and spatial information, a Convolutional Triplet Attention Module(CTAM) is introduced into the feature enhancement network to effectively integrate the information of different dimensions of defect features and improve the model detection accuracy. Additionally, in order to enhance the learning of samples with different degrees of difficulty, the introduction of Focaler-CIoU(F-CIoU) instead of the original boundary regression loss function can optimize the model training process. Finally, the dataset was collected and organized in concrete scenarios from bridge towers, dams, and tunnel corridors, and our method achieved 0.898 average precision, 0.893 average recall, 0.031 average FPR, 0.895 average F1 score, 0.929 mAP50, and 0.731 mAP50:95 on the dataset. The experimental results show that the proposed method achieves the best performance in concrete defect detection.