Wang Hai, Liu Junhao, Dong Haoran, Shao Zheng
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel). 2025 Apr 29;25(9):2794. doi: 10.3390/s25092794.
Multi-sensor fusion object detection is an advanced method that improves object recognition and tracking accuracy by integrating data from different types of sensors. As it can overcome the limitations of a single sensor in complex environments, the method has been widely applied in fields such as autonomous driving, intelligent monitoring, robot navigation, drone flight and so on. In the field of autonomous driving, multi-sensor fusion object detection has become a hot research topic. To further explore the future development trends of multi-sensor fusion object detection, we introduce the mainstream framework Transformer model of the multi-sensor fusion object detection algorithm, and we also provide a comprehensive summary of the feature fusion algorithms used in multi-sensor fusion object detection, specifically focusing on the fusion of camera and LiDAR data. This article provides an overview of feature fusion's development into feature-level fusion and proposal-level fusion, and it specifically reviews multiple related algorithms. We discuss the application of current multi-sensor object detection algorithms. In the future, with the continuous advancement of sensor technology and the development of artificial intelligence algorithms, multi-sensor fusion object detection will show great potential in more fields.
多传感器融合目标检测是一种先进的方法，它通过整合来自不同类型传感器的数据来提高目标识别和跟踪的准确性。由于它可以克服单一传感器在复杂环境中的局限性，该方法已广泛应用于自动驾驶、智能监控、机器人导航、无人机飞行等领域。在自动驾驶领域，多传感器融合目标检测已成为一个热门的研究课题。为了进一步探索多传感器融合目标检测的未来发展趋势，我们介绍了多传感器融合目标检测算法的主流框架Transformer模型，并且还对多传感器融合目标检测中使用的特征融合算法进行了全面总结，特别关注相机和激光雷达数据的融合。本文概述了特征融合向特征级融合和提议级融合的发展，并具体回顾了多个相关算法。我们讨论了当前多传感器目标检测算法的应用。未来，随着传感器技术的不断进步和人工智能算法的发展，多传感器融合目标检测将在更多领域展现出巨大潜力。
