A Model for detection of safety hazards in construction sites using convolutional neural networks

Abstract

Safety should be at the forefront of every construction endeavor as it can be a matter of life or death. As such project success depends heavily on the safety protocols enacted within a site. Safety assurance, has over the past year been under the management of safety professionals. They are responsible for risk analysis, detection of safety hazards, safety protocol compliance, housekeeping etc. This however is quite an onerous task prone to errors and omissions. For example, an individual may miss to detect exposed electrical cabling, a significant safety risk to all within the working area or vicinity. The purpose of this paper is to study the utilization of deep learning techniques in detection of safety hazards on site. The hazards in consideration were fire, electrical hazards and inhalation that can be detected visually. The research workflow constituted data collection, data preparation, model training and validation. Images were collected from various construction sites in Nairobi and augmented by additional data points from the internet. The images were annotated, prepared and augmented using Roboflow, an online annotation and image-preparation tool creating a dataset of 3000 images. The images were split in the ratio of 80:10:10 and then used to train and validate three pre-trained models namely the YOLO version 8, Faster Regional-based Convolutional Neural Network (R-CNN) and Single Shot Detector Single Shot Detector (SSD) models. A mean average precision (mAP) of 0.25, 0.16 and 0.19 was achieved for the respective models, across the three classes or hazards. These results indicated the potential for use of computer vision in safety hazard detection. The You Only look Once (YOLO) version model proved superior in terms of results and was adopted for inference in a web application through a Representational State Transfer (REST) Application Programming Interface (API). This indicated possibility of use in real world setting furthermore, the model could be deployed to a CCTV camera where it can be continually trained to improve its result output. Safety professionals and stakeholders in construction projects would then be able to identify and tackle safety risks in a shorter time span enhancing their capacity to preserve life and health while still adequately managing project resources.

Keywords: Deep learning, Computer vision, Construction, Safety