Abstract
Accurately and effectively identifying defects in concrete reinforcement is crucial for assessing the integrity and long-term durability of building structures. However, this task is costly and requires significant human effort due to the varying appearance of different concrete materials caused by changing weather conditions and lighting intensities, especially the various uncertain graffiti markings, and the random overlapping of defect types on the material surface. Current methods mainly focus on single, non-overlapping defects, which cannot accurately extract features for classification of overlapping defects, resulting in decreased classification performance. To accurately classify overlapping defects in structural defects with multi-label classification, this work proposes an innovative network that incorporates a Spatial and Channel Attention Reconstruction Module (SCARM), and a Spatial Pyramid Pooling Fast (SPPF), to extract defect-representative features from concrete defect images based on EfficientNetV2. Among them, SCARM contributes to assisting the network focus on crucial features and suppressing unnecessary ones and SPPF used to aggregate multi-scale features to addresses the issue of inconsistent input image scales. Additionally, a novel Asymmetric loss (ASL) is introduced to address the imbalance between positive and negative samples in the dataset. As a result, our method achieves a multi-label accuracy of 79.91% and a per-class average accuracy of 95.36% on the CODEBRIM dataset.
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Acknowledgments
This work was supported by the Anhui Province Collaborative Innovation Project (Nos. GXXT-2022-050, GXXT-2022-053), National Natural Science Foundation of China (Nos. 62172004, 62072002, and 61872004), Youth Fund Project of Anhui University of Technology (Nos. QZ202207), Educational Commission of Anhui Province (No. 2022AH050336), Anhui International Joint Research Center for Ancient Architecture Intellisencing and Multi-Dimensional Modeling (No. GJZZX2021KF02) and Open Fund of Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet (No. IASII24-08).
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Che, A., Wang, C., Lu, K., Tao, T., Wang, W., Wang, B. (2024). Multi-label Classification for Concrete Defects Based on EfficientNetV2. In: Huang, DS., Zhang, C., Chen, W. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14865. Springer, Singapore. https://doi.org/10.1007/978-981-97-5591-2_4
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