Abstract
Depth recovery for unstructured farmland road image using an improved SIFT algorithm
Highlights
Intelligent agricultural machinery navigation technology, based on machine vision, has achieved an increasing attention in last decades due to the advantages of rich details of farmland and superior adaptability to complicated environment
Depth information measurement was conducted on the test sites, which was further used to compare with the experimental results for validating the performance of the proposed method in this study
The Canny operator was used to obtain the edge points of the image, which was treated as the feature points
Summary
Intelligent agricultural machinery navigation technology, based on machine vision, has achieved an increasing attention in last decades due to the advantages of rich details of farmland and superior adaptability to complicated environment. Road recognition, coupled with three-dimension (3D) reconstruction is one of the research hotspots. West[1] solved the influence of shadow on image extraction by using color information in road images. Feng et al.[2,3,4] proposed a method for the determination of appropriate driving paths for navigation vehicle by analyzing the color and gray features in different areas. In order to extract the crop center directly under illumination for driving purposes, Bengochea-Guevara et al.[5] used a threshold segmentation method, considering the field contour and crop type. In the study of Bao et Received date: 2018-12-04 Accepted date: 2019-06-11
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