A building safety inspection system with modified ResNet50

Ya-Fen Chen; Xian-Yang Guo; Hao-Dong Chai; Yu-Tong Lin; Yan-Wei Zhu; Jin-Hang Huang; Yu-Hui Zhang; Zne-Jung Lee

doi:10.1117/12.3004676

20 October 2023 A building safety inspection system with modified ResNet50

Ya-Fen Chen, Xian-Yang Guo, Hao-Dong Chai, Yu-Tong Lin, Yan-Wei Zhu, Jin-Hang Huang, Yu-Hui Zhang, Zne-Jung Lee

Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 1291610 (2023) https://doi.org/10.1117/12.3004676
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China

Abstract

The building in some towns or villages have been in disrepair for a long time, and there are hidden safety hazards. However, the procedure for inspecting the risks associated with construction is somewhat antiquated, and considerable personnel and material resources are squandered. Therefore, enterprises hope to make it easier to inspect the hidden dangers of buildings by making a building safety inspection system that can be used more convenient. The building safety inspection system should help enterprises save the manpower and time cost of inspecting hidden dangers, complete the government's entrustment more efficiently, and speed up the enterprise informatization process. It also saves the cost and time of manual statistics, facilitates the process of identifying hidden dangers and management. In this paper, we propose a building safety inspection system with modified ResNet50. This system adopts FastAdmin framework technology to realize the operation process of the building safety inspection, and ECharts technology is used to display charts. Furthermore, the modified ResNet50 is a 50-layer ResNet by using multiple layers, such as convolution layers, pooling layers, and fully connected layers to automatically inspect the building quality. From results, the average testing accuracy for ResNet50 is 99.5%, and the accuracy is better than convolution neural network (CNN). The proposed system can meet the enterprise requirements.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ya-Fen Chen, Xian-Yang Guo, Hao-Dong Chai, Yu-Tong Lin, Yan-Wei Zhu, Jin-Hang Huang, Yu-Hui Zhang, and Zne-Jung Lee "A building safety inspection system with modified ResNet50", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 1291610 (20 October 2023); https://doi.org/10.1117/12.3004676

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