Multiple Classification Network of Concrete Defects Based on Improved EfficientNetV2

The quality of concrete is crucial for the safety of facilities. Specifically, the ex-posed surface defects of the bridge seriously affect its strength and aesthetics. However, due to the influence of weather and light, different types of defects on the concrete surface may potentially overlap, making it difficult for classification algorithms to identify concrete surface defects. Traditional recognition methods based on human observation are unreliable and time-consuming, while automatic recognition methods based on computer vision have limitations in identifying multiple defects simultaneously. In this work, a multi-classification network based on improved EfficientNetV2 [1] is proposed to identify multiple defects simultaneously, in which EfficientNetV2 was used as the backbone to ensure the accuracy of feature extraction, and the spatial pyramid pool structure was combined to achieve multiple label classifications [2]. The results show that the accuracy of the concrete defect multi classification network based on EfficientNetV2 reaches 77.6%, with an average classification accuracy of over 94%. This emphasizes the effectiveness of our method in concrete defect recognition.