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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. [ABSTRACT FROM AUTHOR] more...

In order to solve the problems of poor universality, auxiliary algorithm complexity and great limitation in general segmentation algorithms, a new segmentation algorithm for farmland obstacle images using an intuitionistic fuzzy divergence based on threshold techniques was proposed. The original three-dimensional color image was converted to (Z-Y) chromatic aberration grayscale image on XYZ color space as the input images. While using intuitionistic fuzzy divergence, a modified Wu's membership function and Sugeno's intuitionistic fuzzy generator were used to find the membership and non-membership functions respectively. A new exponential intuitionistic fuzzy divergence based entropy formulas has been proposed and the optimum threshold value has been obtained by minimizing intuitionistic fuzzy divergence. The experimental results indicated that the proposed algorithm could clearly detect all types of obstacles and overcome the influence of unstructured environments well such as uneven illumination, shadow, weather and so on. The results inspire us explore further applications of intuitionistic fuzzy sets in the segmented images that contain a high degree of uncertain information. Furthermore, this proposed method can be used for the agricultural robots vision navigation accurately. [ABSTRACT FROM AUTHOR] more...