Measurement of the distance from grain divider to harvesting boundary based on dynamic regions of interest

Combine harvesters need to work along the crop boundary line during operation. Lateral deviation (the distance between the grain divider and the harvesting boundary line) is important navigation information. Improving the measurement accuracy and real-time performance of lateral deviation is an effective way to improve navigation accuracy. Aiming at the problems of poor real-time performance and low measurement accuracy of existing lateral deviation measurement methods, a method of dynamic selection of the region of interest in the process of image processing was proposed and verified by field experiments. The calculation of lateral deviation includes the following stages: analyzing the average gray value of each column in the field image; drawing dynamic region of interest using maximum average gray value; extracting the rice boundary line by using the probabilistic Hough transform algorithm; predicting the location of the boundary line by using the Kalman filter algorithm; measuring the lateral deviation by using the inverse perspective transform algorithm. The analysis of images under different rice fields showed that the method can effectively identify crop boundary lines. According to the test results of calling cameras in different installation positions, the highest extraction success rate of the boundary line was 96.9%, the average success rate was 94.8%, and the speed of real-time measurement of lateral deviation was 0.065 s/frame. When the driving speed was 0.4 m/s, the detection error of linear tracking detection was less than 4.3 cm. With the increase of speed, the error gradually increased. The algorithm has a good real-time performance and high accuracy during low-speed driving.