DeepHAZMAT：计算资源受限的危险品信号检测和分割
DeepHAZMAT: Hazardous Materials Sign Detection and Segmentation with Restricted Computational Resources
摘要：
在机器人救援行动中，最具挑战性和重要性的任务之一是在作业现场进行危险物质或HAZMAT标志检测，以防止进一步的意外灾难。每个危险品标志都有特定的含义，救援机器人应该检测并解释它，从而采取安全行动。准确的危险品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，我们还必须应付一些次要的挑战，如图像失真和有限的CPU和计算资源，这是嵌入在救援机器人。在本文中，我们提出了一个基于CNN的管道称为DeepHAZMAT检测和分割危险品分为四个步骤; 1）优化输入到CNN网络中的输入图像的数量，2）使用YOLOv 3-微小结构从危险区域收集所需的视觉信息，3）使用GrabCut技术从背景中分割和分离危险品标志，4）利用形态学算子和凸包算法对结果进行后处理。实验结果表明，该方法在占用有限内存和CPU资源的情况下，与现有方法相比，在检测速度和检测精度上都有了较大的提高。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
