Leveraging convolutional neural networks for efficient classification of heavy construction equipment
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42107-024-01159-w.pdf
Reference56 articles.
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2. Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, 101827. https://doi.org/10.1016/J.JOBE.2020.101827
3. Albelwi, S., & Mahmood, A. (2017). A framework for designing the architectures of deep convolutional neural networks. Entropy 2017, 19(6), 242. https://doi.org/10.3390/E19060242. 19.
4. Anirudh, N., Padala, S. P. S., & Reddy, H. N. E. (2023). Development of ANN-Based Risk Prediction Model in Construction Projects. In K. R. Reddy, S. Kalia, S. Tangellapalli, & D. Prakash (Eds.), Recent Advances in Sustainable Environment (pp. 109–121). Springer Nature. https://doi.org/10.1007/978-981-19-5077-3_9
5. Arabi, S., Haghighat, A., & Sharma, A. (2020). A deep-learning-based computer vision solution for construction vehicle detection. Computer-Aided Civil and Infrastructure Engineering, 35(7), 753–767. https://doi.org/10.1111/MICE.12530
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