Detection of Trees Based on Quality Guided Image Segmentation
2014 (English)In: Second International Conference on Robotics and associated High-technologies and Equipment for Agriculture and forestry (RHEA-2014): New trends in mobile robotics, perception and actuation for agriculture and forestry / [ed] Pablo Gonzalez-de-Santos and Angela Ribeiro, 2014, 531-540 p.Conference paper (Refereed)
Detection of objects is crucial for any autonomous field robot orvehicle. Typically, object detection is used to avoid collisions whennavigating, but detection capability is essential also for autonomous or semiautonomousobject manipulation such as automatic gripping of logs withharvester cranes used in forestry. In the EU financed project CROPS,special focus is given to detection of trees, bushes, humans, and rocks inforest environments. In this paper we address the specific problem ofidentifying trees using color images. A presented method combinesalgorithms for seed point generation and segmentation similar to regiongrowing. Both algorithms are tailored by heuristics for the specific task oftree detection. Seed points are generated by scanning a verticallycompressed hue matrix for outliers. Each one of these seed points is thenused to segment the entire image into segments with pixels similar to asmall surrounding around the seed point. All generated segments are refinedby a series of morphological operations, taking into account thepredominantly vertical nature of trees. The refined segments are evaluatedby a heuristically designed quality function. For each seed point, thesegment with the highest quality is selected among all segments that coverthe seed point. The set of all selected segments constitute the identified treeobjects in the image. The method was evaluated with images containing intotal 197 trees, collected in forest environments in northern Sweden. In thispreliminary evaluation, precision in detection was 81% and recall rate 87%.
Place, publisher, year, edition, pages
2014. 531-540 p.
Seed point, Image segmentation, Region growing
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:umu:diva-93290ISBN: 978-84-697-0248-2OAI: oai:DiVA.org:umu-93290DiVA: diva2:747118
Second International Conference on Robotics and associated High-technologies and Equipment for Agriculture and forestry (RHEA-2014)
FunderEU, FP7, Seventh Framework Programme, 246252