The AlexNet HSD model for industrial heritage damage detection and adaptive reuse under artificial intelligence

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.