Zhu Jinsong, Pan Lu, Zhao Ge
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Xu-gong Construction Machinery Group (XCMG) Research Institute, Xuzhou 221116, China.
Sensors (Basel). 2020 Dec 8;20(24):7029. doi: 10.3390/s20247029.
An improved Near-Field Computer Vision (NFCV) system for intelligent fire robot was proposed that was based on our previous works in this paper, whose aims are to realize falling position prediction of jet trajectory in fire extinguishing. Firstly, previous studies respecting the NFCV system were briefly reviewed and several issues during application testing were analyzed and summarized. The improved work mainly focuses on the segmentation and discrimination of jet trajectory adapted to complex lighting environment and interference scenes. It mainly includes parameters adjustment on the variance threshold and background update rate of the mixed Gaussian background method, jet trajectory discrimination based on length and area proportion parameters, parameterization, and feature extraction of jet trajectory based on superimposed radial centroid method. When compared with previous works, the proposed method reduces the average error of prediction results from 1.36 m to 0.1 m, and the error variance from 1.58 m to 0.13 m. The experimental results suggest that every part plays an important role in improving the functionality and reliability of the NFCV system, especially the background subtraction and radial centroid methods. In general, the improved NFCV system for jet trajectory falling position prediction has great potential for intelligent fire extinguishing by fire-fighting robots.
本文基于我们之前的工作，提出了一种用于智能消防机器人的改进型近场计算机视觉（NFCV）系统，其目的是实现灭火过程中射流轨迹落点位置的预测。首先，简要回顾了之前关于NFCV系统的研究，并分析总结了应用测试过程中出现的几个问题。改进工作主要集中在适应复杂光照环境和干扰场景的射流轨迹分割与判别上。主要包括对混合高斯背景法的方差阈值和背景更新率进行参数调整、基于长度和面积比例参数的射流轨迹判别、基于叠加径向质心法的射流轨迹参数化及特征提取。与之前的工作相比，该方法将预测结果的平均误差从1.36米降低到0.1米，误差方差从1.58米降低到0.13米。实验结果表明，各部分在提高NFCV系统的功能和可靠性方面都发挥着重要作用，尤其是背景减法和径向质心法。总体而言，改进后的用于射流轨迹落点位置预测的NFCV系统在消防机器人智能灭火方面具有巨大潜力。
