Off-road intelligent vehicle is an important application about Internet of Vehicles technology used in the transportation field, and the front obstacle recognition method is the key technology for off-road intelligent vehicle. In this paper, based on smart data aggregation inspired paradigm of IoT applications, we mainly study perception technology in vehicle networking by using image data and one symmetrical speeded-up robust features detector (SURF). By considering symmetry and image data aggregation, we found that data aggregation had the ability of providing global information for Internet of Vehicles systems. After we have built the experiment platform, the experiment results showed that this method is faster than Scale-Invariant Feature Transform algorithm in this case, which can satisfy the water detection accuracy and the real-time requirement. So, this method is effective for the water images detection with great symmetry to off-road intelligent vehicle, and it also gives a useful reference about environment perception technology and smart data aggregation inspired paradigm used in future Internet of Vehicles, intelligent vehicle, and traffic safety applications.

中文翻译：

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基于图像数据的越野智能车前水识别方法

越野智能车是交通领域中有关车联网技术的重要应用，前方障碍物识别方法是越野智能车的关键技术。本文基于物联网应用的智能数据聚合启发范式，我们主要通过使用图像数据和一个对称的加速鲁棒特征检测器（SURF）研究车辆联网中的感知技术。通过考虑对称性和图像数据聚合，我们发现数据聚合具有为车联网系统提供全局信息的能力。建立实验平台后，实验结果表明该方法比尺度不变特征变换算法要快。可以满足水的检测精度和实时性要求。因此，该方法对于与越野智能车具有高度对称性的水图像检测是有效的，并且对于环境感知技术和智能数据聚合启发范式在未来的车联网，智能车和交通中使用提供了有用的参考。安全应用。