Automated Data Labeling for Object Detection via Iterative Instance Segmentation

Jinyoon Kim, Md Faisal Kabir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Data labeling in computer vision, specifically in object detection tasks, remains a significant challenge in terms of efficiency and accuracy. This research article introduces an auto-labeling algorithm that combines active deep-learning techniques with the YOLOv8 model. The aim is to automate the data labeling process and enhance the performance of the object detection model. The proposed algorithm automatically labels a portion of unlabeled data based on uncertainty scores, integrating it into the training dataset. This approach reduces the need for manual annotation, which can be time-consuming. The effectiveness of the method is evaluated using two datasets: tomato and apple. The results demonstrate a substantial improvement in the Mean Average Precision score over multiple iterations, highlighting the enhanced performance of the overall model. Moreover, the experiments show that the proposed algorithm surpasses traditional manual annotation methods by generating a higher-performing model with significantly less annotation effort.

Original languageEnglish (US)
Title of host publicationProceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
EditorsM. Arif Wani, Mihai Boicu, Moamar Sayed-Mouchaweh, Pedro Henriques Abreu, Joao Gama
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages845-850
Number of pages6
ISBN (Electronic)9798350345346
DOIs
StatePublished - 2023
Event22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 - Jacksonville, United States
Duration: Dec 15 2023Dec 17 2023

Publication series

NameProceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

Conference

Conference22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
Country/TerritoryUnited States
CityJacksonville
Period12/15/2312/17/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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