Intelligent Annotation Algorithm Based on Deep-Sea Macrobenthic Images

Authors

  • Qihang Wu College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China
  • Yong Liu College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China
  • Jianyi Zhang College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China
  • Yongpan Wang College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China

DOI:

https://doi.org/10.31577/cai_2022_3_739

Keywords:

Intelligent labeling, active learning, pseudo-labeling, object detection

Abstract

In the field of image processing, due to the need of expertise and skills in deep-sea biology and the disadvantages of high labor cost and long time consuming, it has always been a difficult task to mark the images of deep-sea benthic organisms. To solve this problem, this paper proposes a new image intelligent labeling algorithm LACP AL (Localization-Aware-Choice and Pseudo Label Active Learning) which is based on Localization-Aware Active Learning. LACP AL is an active learning framework based on Faster R-CNN, it finds the "valuable" samples from unlabeled samples by clustering algorithm for every training; it selects hard-to-identify samples for manual annotation and further optimizes the model; and it proposes an improved pseudo-labeling mechanism to expand the training set and improve the model accuracy. According to the publicly available dataset provided by 2020 China Underwater Robot Professional Contest, a series of experiments has been done to verify that our algorithm can achieve higher recognition accuracy with fewer training samples compared with the existing algorithms for Marine benthic image recognition.

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Published

2022-09-08

How to Cite

Wu, Q., Liu, Y., Zhang, J., & Wang, Y. (2022). Intelligent Annotation Algorithm Based on Deep-Sea Macrobenthic Images. Computing and Informatics, 41(3), 739–756. https://doi.org/10.31577/cai_2022_3_739