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Smart Structures and Systems Volume 36, Number 4, October 2025 , pages 213-222 DOI: https://doi.org/10.12989/sss.2025.36.4.213 |
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Adaptive object detection for construction sites via parameter-efficient fine-tuning with LoRA |
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Hyung-soo Kim, Jaehwan Seong, Yuree Choi and Hyung-Jo Jung
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| Abstract | ||
| Construction sites present diverse and evolving visual conditions that challenge the generalizability of pre-trained object detection models. This study proposes a parameter-efficient fine-tuning approach based on Low-Rank Adaptation to enable adaptive object detection tailored to site-specific conditions. A general model was trained on a large-scale dataset and fine-tuned using both the proposed method and full fine-tuning across three real-world construction projects. Despite utilizing only 12% of the trainable parameters, the proposed approach achieved comparable or superior detection accuracy with 10% reduced training time and 30% lower GPU memory consumption. These results highlight its effectiveness in adapting object detection models to site-specific conditions under resource constraints. Furthermore, the approach can be extended with semisupervised learning to support scalable adaptation in construction environments. | ||
| Key Words | ||
| adaptive object detection; computer vision; construction safety Low-Rank Adaptation (LoRA); parameterefficient fine-tuning | ||
| Address | ||
| Department of Civil and Environmental Engineering, Korea Advanced Institute for Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea. | ||