Helmet

IOT BASED HELMET DETECTION SYSTEM FOR INDUSTRIES USING DEEP LEARNING Workplace safety is crucial for each and every worker in the industry because all the workers desire to work in a safe and protected atmosphere. Having no proper workplace safety lead to the cause of injuries. Most of the industries have used latest technology based on Artificial Intelligence such as deep learning, machine learning. Improved safety system based on object detection with deep learning can help save lives and reduce injury. The major purpose of the research is to implement a surveillance system to detect the use of helmet in workplaces such as industries, construction sites and to monitor the workers’ safety. The system in this paper is the combination of computer vision with deep learning and IOT to reduce the accidents in industry. The proposed system can provide a real time monitoring and live video streaming of the area of workspaces through a web browser interface. Helmet detection system is constructed based on Deep Learning based model, Faster RCNN inceptionv2, and TensorFlow object detection model. As a dataset for helmet detection, images are collected from Google and YouTube videos. Hyperparameters are tuned step-by-step before training helmet detection model. After training the model, classifying and localizing are implemented on video from webcam. The video of detection of helmet is sent directly to website created only for the purpose of monitoring safety condition. The experimental results show strong robustness on various condition although the model has over fitting problem. This helmet detection system can also be used not only in construction and industrial sites but also in other safety areas.

References

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