Comprehensive image datasets can benefit the construction industry in terms of serving as the basis for generating deep-learning-based object detection models and testing the performance of object detection algorithms, but building such datasets is complex and requires vast professional knowledge. This paper develops and publicly releases a new large-scale image dataset specifically collected and annotated for the construction site, called Site Object Detection dAtaset (SODA), which contains 15 object classes categorized by the worker, material, machine, and layout. More than 20,000 images were collected from multiple construction sites in different situations, weather conditions, and construction phases, covering different angles and perspectives. Statistical analysis shows that the dataset is well developed in terms of diversity and volume. Further evaluation with two widely adopted deep learning-based object detection algorithms also illustrates the feasibility of the dataset, achieving a maximum mAP of 81.47%. This research contributes a large-scale open image dataset for the construction industry and sets up a performance benchmark for further evaluation of relevant algorithms.
The authors would like to acknowledge the support by Guangdong Science Foundation (Grant No. 2022A1515010174); the support by the State Key Lab of Subtropical Building Science, South China University of Technology (No. 2022ZB19); the support by the Guangzhou Science and Technology Program (No. 202201010338); and the National Natural Science Foundation of China (Grant No. 72091512, 51908323).
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