Research on binocular visual impairment perception of a cultivator boat based on YOLOv5
In order to satisfy the automatic driving function of the boat tractor,this paper designed a set of YOLOv5 integrated SGBM algorithm machine vision obstacle perception system.Firstly,people,machine-tiller and farm tools were taken as objects to shoot and collect images to get paddy field obstacle data set.The images were input into the YOLOv5 network model for iterative training to get the optimal weight.Later,the most weight was used for testing and compared with YOLOv4 and Faster R-CNN networks.The left and right images taken by the binocular camera were input into the YOLOv5 model for detection.After correcting and transforming the output information of the target obstacle detection box,the SGBM algorithm was used for parallax calculation to complete the target obstacle recognition and depth estimation.The results show that the average accuracy of YOLOv5 is stable at 87.25％,1.55％higher than that of YOLOv4,4.04％higher than that of Faster R-CNN,and the detection time of a single image is 0.017 s,0.081 s faster than that of YOLOv4.It is 0.182 s faster than Faster R-CNN,and the model size is only 13.7 MB,236.4 MB smaller than YOLOv4.The confidence of the YOLOv5 network model is 0.91,0.99 and 0.95 respectively when detecting the boat tractor,man and farm tools.The depth estimation of YOLOv5+SGBM within 2 m,and the accuracy reaches 98.1％.The paddy field depth estimation based on YOLOv5 and SGBM can meet the actual requirements of unmanned boat tractor with rotary tillage.
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