  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.
