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Lightweight Model-Based Intrusion Detection in Construction Scenes

Haoyang Zhao
Vol. 3, No. 1, pp. 58-60, 2025 DOI: 10.54097/ejhnrv33 April 27, 2025
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Abstract.Intrusion detection in construction scenes can effectively reduce the occurrence of hazardous incidents. Current detection methods, while effective, are often too complex. This paper proposes a lightweight monitoring model based on convolutional neural networks (CNNs). First, the model is trained using a dataset to achieve high accuracy. Then, the model is lightweighted using CNNs. Simulation results show that the model can maintain accuracy while occupying a smaller volume.
Keywords. Safety helmet detection Yolov5 Convolutional Neural Network
APA Citation Haoyang Zhao (2025). Lightweight Model-Based Intrusion Detection in Construction Scenes. Journal of Computer Science and Artificial Intelligence, 3(1), 58-60. https://doi.org/10.54097/ejhnrv33

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