A deep learning based hazardous materials (HAZMAT) sign detection robot with restricted computational resources

Abstract

One of the most challenging and non-trivial tasks in robot-based rescue operations is the Hazardous Materials (HAZMAT) sign detection in dangerous operation fields, in order to prevent further unexpected disasters. Each HAZMAT sign has a specific meaning that the rescue robot should detect and interpret to take a safe action, accordingly. Accurate HAZMAT detection and real-time processing are the two most important factors in such robotics applications . Furthermore, the rescue robot should cope with some secondary challenges such as image distortion and restricted CPU and computational resources , embedded in the robot. In this research, we propose a CNN-Based pipeline called DeepHAZMAT for HAZMAT sign detection and segmentation in four steps: (1) Input data volume optimisation before feeding into the CNN network, (2) Application of a YOLO-based structure to collect the required visual information from the hazardous areas, (3) HAZMAT sign segmentation and separation from the background using adaptive GrabCut technique, and (4) Post-processing optimisation using morphological operators and convex hull algorithms. In spite of the utilisation of a very limited CPU and memory resources, the experimental results show the proposed method has successfully maintained a better performance in terms of detection-speed and detection-accuracy, compared to classical and modern state-of-the-art methods.

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