论文标题
DeepHazmat：危险材料标志检测和分割有限的计算资源
DeepHAZMAT: Hazardous Materials Sign Detection and Segmentation with Restricted Computational Resources
论文作者
论文摘要
基于机器人的救援行动中，最具挑战性和最不平凡的任务之一是操作领域中的危险材料或Hazmats签名检测，以防止进一步的意外灾难。每个Hazmat标志都有一个特定的含义，即救援机器人应检测并解释其采取安全措施。准确的Hazmat检测和实时处理是此类机器人应用中最重要的两个因素。此外，我们还必须应对嵌入在救援机器人中的一些次要挑战，例如图像失真和受限的CPU和计算资源。在本文中，我们提出了一种基于CNN的管道，称为DeepHazmat，用于以四个步骤检测和分割Hazmats。 1）优化馈入CNN网络的输入图像的数量，2）使用Yolov3小型结构从危险区域收集所需的视觉信息，3）使用GrabCut技术，以及使用GrabCut技术与背景分离，以及4）4）与形态算子和污染物Hull Algorithm一起处理结果。尽管利用非常有限的内存和CPU资源，但实验结果表明，与最先进的方法相比，该方法在检测速度和检测准确性方面成功保持了更好的性能。
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.
