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Interactive object annotation based on one-click guidance

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Abstract

Due to the large workload of manual annotation of datasets, uneven data quality and high professional thresholds have been a problem. Based on the idea of semi-automatic annotation, this article discusses the method use of interactive methods to obtain accurate annotations of objects. We propose a method of human–machine interactive object annotation based on one-click guidance. Specifically, we click on a point close to the center of the object and use the prior information of this point to give a guide to the model. The advantages of our method are fourfold: (1) the simulated click method is transferable and can be labeled across datasets; (2) clicks help to eliminate irrelevant areas within the bounding box; (3) the operation is more convenient and does not require artificial boxes, we only need to give the relevant location information; (4) our method supports additional click annotations for further correction. To verify the effectiveness of the proposed method, we conducted a lot of experiments on the KITTI and PASCAL VOC2012 datasets, and the results proved that our model has improved average IoU by 18.1% and 14.6% compared with Anno-Mage and CVAT, respectively. Our method focuses on improving the accuracy and efficiency of annotation, and provides a new idea for the field of semi-automatic annotation.

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Author notes

  1. Yijin Xiong and Xin Gao have contributed equally to this work.

Authors and Affiliations

  1. Computer Science and Technology, China University of Mining and Technology-Beijing, Xueyuan Road, Beijing, 100083, China

    Yijin Xiong, Xin Gao & Guoying Zhang

Authors
  1. Yijin Xiong
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  2. Xin Gao
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  3. Guoying Zhang
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Contributions

GZ and XG wrote the main manuscript text, and YX prepared Figs. 1, 2 and 3, and GZ prepared Figs. 4 and 5, and XG prepared Figs. 6 and 7. All authors reviewed the manuscript.

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Correspondence to Guoying Zhang.

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Xiong, Y., Gao, X. & Zhang, G. Interactive object annotation based on one-click guidance. J Supercomput 79, 16098–16117 (2023). https://doi.org/10.1007/s11227-023-05279-z

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