TITLE: SODA: Site Object Detection dAtaset for Deep Learning in Construction 

ABSTRACT: Computer vision-based deep learning object detection algorithms have been
developed sufficiently powerful to support the ability to recognize various
objects. Although there are currently general datasets for object detection,
there is still a lack of large-scale, open-source dataset for the construction
industry, which limits the developments of object detection algorithms as they
tend to be data-hungry. Therefore, this paper develops a new large-scale image
dataset specifically collected and annotated for the construction site, called
Site Object Detection dAtaset (SODA), which contains 15 kinds of object classes
categorized by workers, materials, machines, and layout. Firstly, more than
20,000 images were collected from multiple construction sites in different site
conditions, weather conditions, and construction phases, which covered
different angles and perspectives. After careful screening and processing,
19,846 images including 286,201 objects were then obtained and annotated with
labels in accordance with predefined categories. Statistical analysis shows
that the developed dataset is advantageous in terms of diversity and volume.
Further evaluation with two widely-adopted object detection algorithms based on
deep learning (YOLO v3/ YOLO v4) also illustrates the feasibility of the
dataset for typical construction scenarios, achieving a maximum mAP of 81.47%.
In this manner, this research contributes a large-scale image dataset for the
development of deep learning-based object detection methods in the construction
industry and sets up a performance benchmark for further evaluation of
corresponding algorithms in this area.
Computer vision-based deep learning object detection algorithms have been
developed sufficiently powerful to support the ability to recognize various
objects. Although there are currently general datasets for object detection,
there is still a lack of large-scale, open-source dataset for the construction
industry, which limits the developments of object detection algorithms as they
tend to be data-hungry. Therefore, this paper develops a new large-scale image
dataset specifically collected and annotated for the construction site, called
Site Object Detection dAtaset (SODA), which contains 15 kinds of object classes
categorized by workers, materials, machines, and layout. Firstly, more than
20,000 images were collected from multiple construction sites in different site
conditions, weather conditions, and construction phases, which covered
different angles and perspectives. After careful screening and processing,
19,846 images including 286,201 objects were then obtained and annotated with
labels in accordance with predefined categories. Statistical analysis shows
that the developed dataset is advantageous in terms of diversity and volume.
Further evaluation with two widely-adopted object detection algorithms based on
deep learning (YOLO v3/ YOLO v4) also illustrates the feasibility of the
dataset for typical construction scenarios, achieving a maximum mAP of 81.47%.
In this manner, this research contributes a large-scale image dataset for the
development of deep learning-based object detection methods in the construction
industry and sets up a performance benchmark for further evaluation of
corresponding algorithms in this area.
SODA: Site Object Detection dAtaset for Deep Learning in Construction
http://arxiv.org/abs/2202.09554v1