Large AI Models for Building Material Counting Task: A Comparative Study

The rapid advancement of general large models has significantly impacted and introduced new concepts to the traditional “one task, one model” research paradigm in construction automation. In this paper, we evaluate the performance of existing large models and those developed on large model platforms, using building material counting as an example. We compare three categories of large AI models for building material counting, including multimodal large models, purely visual large models, and secondary models developed on platforms. Through this research, we aim to explore the accuracy and practicality of these models in real-world construction scenarios. The results indicate that directly applying general large models faces challenges in processing photos with complex shapes or backgrounds, failing to provide accurate counting results. Additionally, while purely visual large models excel in instance segmentation tasks, their application to the specific counting of building materials requires additional programming work. To address these issues, this study explores solutions based on large model secondary development platforms and trains a model using EasyDL as an example. Leveraging deep learning techniques, this model achieves effective counting of building materials through five steps: data preparation, model type selection, model training, model validation, and model deployment. Although models developed based on large model platforms are presently less accurate than specialized models, they still represent a highly promising approach.