Full
Outline
In order to improve the accuracy of unmanned agricultural machine's perception of obstacles in the farm environment, to solve the problem that visual detection is easily affected by light and millimeter-wave radar detection is easily affected by vehicle bumps, etc., as well as the problem that the visual target detection algorithm has a large number of parameters, a large amount of computation, and a large volume of the model under the complex field, this paper proposes an obstacle-avoidance target detection method for unmanned agricultural machines with the fusion of visual and millimeter-wave radar information. Part of the radar target data from millimeter-wave radar is first filtered, and a target tracking algorithm based on adaptive extended Kalman filtering is proposed. Then a farm environment obstacle dataset is produced and a target detection network based on improved YOLO v5s is constructed. Subsequently, the mapping of radar points into the image pixel coordinate system is realized by time-stamp alignment and coordinate transformation with direct linear calibration method. Finally, the fusion model of obstacle detection information from millimeter-wave radar and visual sensors is constructed through the decision-level fusion method and target matching strategy, and the experimental results show that the mean average accuracy of the improved YOLO v5s is 97.0%, which is similar to that of the original model, but the number of parameters, the amount of computation, and the size of the model are only 40.2%, 39.2%, and 38.2% of the original YOLO v5s model, respectively. Compared with YOLO v4-Tiny, YOLO v7-Tiny, YOLO v4 and YOLO v7 models, it can better balance the detection accuracy and speed. The results of multi-scene tests show that the fusion method proposed in this paper improves the recognition accuracy by 2.67 percentage points and 15.07 percentage points compared with radar and camera in daytime tests, and the fusion detection method can effectively make up for the failure of the camera in nighttime tests, and has better robustness and accuracy than the single-sensor algorithm, and the fusion obstacle avoidance system effectively realizes the parking avoidance of the unmanned agricultural machine.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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