Safety Helmet Detection in Industrial Environment using Deep Learning

Abstract

Safety is of predominant value for employees who are working in an industrial and construction environment. Real time Object detection is an important technique to detect violations of safety compliance in an industrial setup. The negligence in wearing safety helmets could be hazardous to workers, hence the requirement of the automatic surveillance system to detect persons not wearing helmets is of utmost importance and this would reduce the labor-intensive work to monitor the violations. In this paper, we deployed an advanced Convolutional Neural Network (CNN) algorithm called Single Shot Multibox Detector (SSD) to monitor violations of safety helmets. Various image processing techniques are applied to all the video data collected from the industrial plant. The practical and novel safety detection framework is proposed in which the CNN first detects persons from the video data and in the second step it detects whether the person is wearing the safety helmet. Using the proposed model, the deep learning inference benchmarking is done with Dell Advanced Tower workstation. The comparative study of the proposed approach is analysed in terms of detection accuracy (average precision) which illustrates the effectiveness of the proposed framework.