Automatic Detection of Helmet Uses for Construction Safety

The U.S. construction industry suffers from the highest number of fatalities among all industries, i.e., one in five worker deaths in private industry were in construction. Tremendous loss has occurred to the workers' families, the industry, and the nation. Considering the large and increasing number of construction projects that are being conducted in the U.S., there is a growing necessity of developing innovative methods to automatically monitor the safety for the workers at construction sites. Since the head is the most critical area of a human body and is the most vulnerable to an impact that could cause serious injury or death, the use of a protective helmet in construction work is needed. In this paper, we aim to automatically detect the uses of construction helmets (e.g., whether the construction worker wears the helmet or not) by analyzing the construction surveillance images. Based on the collected images, we first detect the object of interest (i.e., construction worker) and further analyze whether the worker wears the helmet or not, by using computer vision and machine learning techniques. In the first step, we incorporate frequency domain information of the image with a popular human detection algorithm Histogram of Oriented Gradient (HOG) for construction worker detection; in the second step, the combination of color-based and Circle Hough Transform (CHT) feature extraction techniques is applied to detect helmet uses for the construction worker.