A pixel-level coarse-to-fine image segmentation labelling algorithm.

Abstract

Fine segmentation labelling tasks are time consuming and typically require a great deal of manual labor. This paper presents a novel method for efficiently creating pixel-level fine segmentation labelling that significantly reduces the amount of necessary human labor. The proposed method utilizes easily produced multiple and complementary coarse labels to build a complete fine label via supervised learning. The primary label among the coarse labels is the manual label, which is produced with simple contours or bounding boxes that roughly encompass an object. All others coarse labels are complementary and are generated automatically using existing algorithms. Fine labels can be rapidly created during the supervised learning of such coarse labels. In the experimental study, the proposed technique achieved a fine label IOU (intersection of union) of 92% in segmenting our newly constructed bean field dataset. The proposed method also achieved 95% and 92% mean IOU when tested on publicly available agricultural CVPPP and CWFID datasets, respectively. Our proposed method of segmentation also achieved a mean IOU of 81% when it was tested on our newly constructed paprika disease dataset, which includes multiple categories.

Plant phenotyping relevance

画素レベル画像セグメンテーション手法そのものを開発し、植物・農業およびパプリカ病害データセットで性能評価しているため、植物画像から状態・領域を抽出する方法研究として中心的です。

Code and data availability

The paper's authors explicitly state their analysis code is publicly available on GitHub. The newly constructed plant datasets (Bean-Field, Paprika-Disease, Circle) are only available on request, so they qualify as request_only, not public.