A Novel Edge Computing Framework for Construction Nail Detection under Conditions of Constrained Computing Resources

Le résumé fourni par la source

Construction housekeeping promotes a safe working environment but often lacks an effective approach for monitoring and maintenance. Recent research has used artificial intelligence (AI) techniques to detect construction objects automatically to facilitate the work of safety managers. However, none of the research has considered implementing real-time AI applications for automatic construction housekeeping monitoring. We propose a framework that integrates edge computing and a computer vision model to detect boards with nails that may be scattered throughout construction sites. First, we trained a MobileNet machine learning (ML) model to identify boards with or without nails. Then, we quantized the model using TensorFlow Lite to allow the model’s optimal deployment in edge devices. Lastly, we assembled an edge device module based on Raspberry Pi and embedded the ML model to realize real-time offline housekeeping monitoring. The experimental results show great promise for both lab settings and in practice at construction sites. The proposed framework can facilitate AI applications in various construction fields under computing resource-constrained conditions.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.