DeepHAZMAT: Hazardous Materials Sign Detection and Segmentation with Restricted Computational Resources

TL;DR

DeepHAZMAT is a resource-efficient CNN pipeline that accurately detects and segments hazardous materials signs in real-time, even with limited computational resources, aiding rescue robots in hazardous environments.

Contribution

The paper introduces a novel CNN-based pipeline combining YOLOv3-tiny, GrabCut, and post-processing techniques for efficient Hazmat sign detection and segmentation under resource constraints.

Findings

Achieves real-time detection with limited CPU and memory.

Outperforms state-of-the-art methods in detection accuracy.

Maintains high segmentation quality despite resource limitations.

Abstract

One of the most challenging and non-trivial tasks in robot-based rescue operations is the Hazardous Materials or HAZMATs sign detection in the operation field, to prevent further unexpected disasters. Each Hazmat sign has a specific meaning that the rescue robot should detect and interpret it to take a safe action, accordingly. Accurate Hazmat detection and real-time processing are the two most important factors in such robotics applications. Furthermore, we also have to cope with some secondary challenges such as image distortion and restricted CPU and computational resources which are embedded in a rescue robot. In this paper, we propose a CNN-Based pipeline called DeepHAZMAT for detecting and segmenting Hazmats in four steps; 1) optimising the number of input images that are fed into the CNN network, 2) using the YOLOv3-tiny structure to collect the required visual information from the hazardous areas, 3) Hazmat sign segmentation and separation from the background using GrabCut technique, and 4) post-processing the result with morphological operators and convex hull algorithm. In spite of the utilisation of a very limited memory and CPU resources, the experimental results show the proposed method has successfully maintained a better performance in terms of detection-speed and detection-accuracy, compared with the state-of-the-art methods.