Deep Learning Based Fire Risk Detection on Construction Sites

Abstract

The recent large-scale fire incidents on construction sites in South Korea have highlighted the need for computer vision technology to detect fire risks before an actual occurrence of fire. This study developed a proactive fire risk detection system by detecting the coexistence of an ignition source (sparks) and a combustible material (urethane foam or Styrofoam) using object detection on images from a surveillance camera. Statistical analysis was carried out on fire incidences on construction sites in South Korea to provide insight into the cause of the large-scale fire incidents. Labeling approaches were discussed to improve the performance of the object detectors for sparks and urethane foams. Detecting ignition sources and combustible materials at a distance was discussed in order to improve the performance for long-distance objects. Two candidate deep learning models, Yolov5 and EfficientDet, were compared in their performance. It was found that Yolov5 showed slightly higher mAP performances: Yolov5 models showed mAPs from 87% to 90% and EfficientDet models showed mAPs from 82% to 87%, depending on the complexity of the model. However, Yolov5 showed distinctive advantages over EfficientDet in terms of easiness and speed of learning.

DCP-Net: An Efficient Image Segmentation Model for Forest Wildfires

“…Zhu et al [33] used an improved YOLOv7-tiny model to detect cabin fires, resulting in a 2.6% increase in mAP@0.5 and a 10 fps increase in frame rate. Hojune Ann et al [34] developed a fire risk detection system that detects fire sources and combustible materials simultaneously by object detection on images captured by surveillance cameras, comparing the performance of two deep learning models, YOLOv5 and EfficientDet.…”