Deep learning-based object detection for visible dust and prevention measures on construction sites

Activities on construction sites inevitably generate large amounts of dust, which can pose a significant threat to air quality and public health. Detection of construction dust has received increasing attention. In this study, we proposed an object detection method for visible dust and relevant prevention measures on construction sites based on YOLOv7 detector. Deformable Convolutional Networks and Wise-IoU were introduced to improve the precision of the detector in detecting non-rigid objects, such as visible dust. We constructed a publicly available dataset containing 7,500 realistic images covering various construction site conditions. The results indicate that the improved detector achieved high precision with mean average precision of 68.1% and detection speed of 80.6 frames per second. The proposed method enables real-time and effective detection of visible dust and relevant prevention measures on construction sites, which will help advance the research of dust detection and prevention in open space environments.