Multi-crop-row detection algorithm based on binocular vision

Pathway determination is an important process in vision-based navigation. The pathway is very difficult to determine simply using 2D image processing, because fields are often infested with weeds, and images contain shadows, illumination variation, irregular backgrounds and other unexpected noise. Stereo vision techniques can be used to locate the spatial positions of crop rows for pathway determination. However, the stereo matching of field images is generally time-consuming and insufficiently accurate. To solve this problem, a multi-crop-row detection algorithm based on binocular vision is proposed in this paper. The algorithm is composed of the modules of image preprocessing, stereo matching and centreline detection of multiple crop rows. An accurate stereo matching method was put forward to locate the 3D position of crop rows based on the rank transformation, Harris detector and random sample consensus methods. A new method for detecting the centrelines of multiple crop rows was proposed according to their spatial distribution. The proposed algorithm was validated by comparative experiments. Regarding the proposed algorithm in situations without turnrows, the correct detection rate is greater than 92.78%; for the average deviation angle, the absolute average value is less than 1.05 degrees, and the average standard deviation is less than 3.66 degrees; for the processing time, the average value is less than 634 ms, and the average standard deviation is less than 101 ms. The results indicate that the proposed algorithm can satisfy the requirements of accuracy and realtime execution in field operation. (C) 2016 IAgrE. Published by Elsevier Ltd. All rights reserved.