ArticleSensors (Basel, Switzerland)2022
Multi-Objective Association Detection of Farmland Obstacles Based on Information Fusion of Millimeter Wave Radar and Camera.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A Survey of Environmental Perception for Unmanned Ground Agricultural Machinery in Field Environments.Sensors (Basel, Switzerland) · 2026Review
- Fusion Ranging Method of Monocular Camera and Millimeter-Wave Radar Based on Improved Extended Kalman Filtering.Sensors (Basel, Switzerland) · 2025Article
- Forward Collision Warning Strategy Based on Millimeter-Wave Radar and Visual Fusion.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
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Abstract
In order to remedy the defects of single sensor in robustness, accuracy, and redundancy of target detection, this paper proposed a method for detecting obstacles in farmland based on the information fusion of a millimeter wave (mmWave) radar and a camera. Combining the advantages of the mmWave radar in range and speed measurement and the camera in type identification and lateral localization, a decision-level fusion algorithm was designed for the mmWave radar and camera information, and the global nearest neighbor method was used for data association. Then, the effective target sequences of the mmWave radar and the camera with successful data association were weighted to output, and the output included more accurate target orientation, longitudinal speed, and category. For the unassociated sequences, they were tracked as new targets by using the extended Kalman filter algorithm and were processed and output during the effective life cycle. Lastly, an experimental platform based on a tractor was built to verify the effectiveness of the proposed association detection method. The obstacle detection test was conducted under the ROS environment after solving the external parameters of the mmWave radar and the internal and external parameters of the camera. The test results show that the correct detection rate of obstacles reaches 86.18%, which is higher than that of a single camera with 62.47%. Furthermore, through the contrast experiment of the sensor fusion algorithms, the detection accuracy of the decision level fusion algorithm was 95.19%, which was higher than 4.38% and 6.63% compared with feature level and data level fusion, respectively.
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Registered trials
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