Zhang Huiling
History and Social Work College, Chongqing Normal University, Chongqing, 401331, China.
Sci Rep. 2025 Jul 19;15(1):26289. doi: 10.1038/s41598-025-12257-3.
As the importance of preserving and utilizing industrial heritage continues to grow, improving the efficiency and accuracy of damage detection for industrial heritage has become a key research focus. This work optimizes the structure of the traditional AlexNet HSD (Alex Krizhevsky Network Hierarchical Structure Detection) model. By integrating the Convolutional Block Attention Module (CBAM) and Support Vector Machine (SVM), an AlexNet HSD + CBAM + SVM (AlexNet HCS) model is proposed to enhance the performance of industrial heritage damage detection. Experiments are conducted on a comprehensive dataset composed of the xView2 Building Damage Assessment Dataset (xBD) and photos of third-line construction buildings in Southwest China. The results show that through structural improvements and the combination of the CBAM module and SVM, the AlexNet HCS model achieves an accuracy of 95.7%, an increase of 12.2% compared with AlexNet HSD. Its Precision, Recall, and F1 score are 94.8%, 95.7%, and 95.2% respectively, verifying the effectiveness of the optimization strategy. Ablation experiments verify the improvement of network structure and the synergistic gain of CBAM and SVM. CBAM only increases 3.5% Floating Point Operations (FLOPs) and 4ms reasoning delay, but brings 1.8% accuracy improvement; Placing DropBlock in Conv5 can further inhibit over-fitting. In comparative experiments with other models, AlexNet HCS demonstrates superior classification performance and faster convergence speed, proving its efficacy in building damage identification. Moreover, based on the findings in damage detection, this work proposes specific pathways for the adaptive reuse of industrial heritage from the Third Front Construction in Southwest China. It aims to support the sustainable development and cultural preservation of industrial heritage. This work intends to provide novel technical support and theoretical foundation for the protection of industrial heritage, promoting its scientific and sustainable utilization.
随着保护和利用工业遗产的重要性不断提高，提高工业遗产损伤检测的效率和准确性已成为关键研究重点。这项工作优化了传统AlexNet HSD（亚历克斯·克里兹维斯基网络层次结构检测）模型的结构。通过集成卷积块注意力模块（CBAM）和支持向量机（SVM），提出了AlexNet HSD+CBAM+SVM（AlexNet HCS）模型，以提高工业遗产损伤检测的性能。在由xView2建筑损伤评估数据集（xBD）和中国西南三线建设建筑照片组成的综合数据集上进行了实验。结果表明，通过结构改进以及CBAM模块和SVM的结合，AlexNet HCS模型的准确率达到95.7%，比AlexNet HSD提高了12.2%。其精确率、召回率和F1分数分别为94.8%、95.7%和95.2%，验证了优化策略的有效性。消融实验验证了网络结构的改进以及CBAM和SVM的协同增益。CBAM仅增加了3.5%的浮点运算（FLOPs）和4毫秒的推理延迟，但带来了1.8%的准确率提升；在Conv5中放置DropBlock可以进一步抑制过拟合。在与其他模型的对比实验中，AlexNet HCS表现出卓越的分类性能和更快的收敛速度，证明了其在建筑损伤识别中的有效性。此外，基于损伤检测的结果，这项工作提出了中国西南三线建设工业遗产适应性再利用的具体途径。旨在支持工业遗产的可持续发展和文化保护。这项工作旨在为工业遗产保护提供新颖的技术支持和理论基础，促进其科学和可持续利用。
