Locating Image Objects with Probability Distributions

Yu Hsiang Lin, Chih Jen Hsu, Chih Hung Kuo, Ming Der Shieh

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

In this letter, we predict the locations as probability distributions for the tasks of image object detection. We adopt the Kullback-Leibler divergences as the regression losses to train the deep neural networks. Since most existing evaluations label the objects with rectangular bounding boxes, we propose the Nearest Distribution Converter to find the closest uniform distributions from the predicted ones. Our proposed method can improve the detected accuracy measured in mAP by 0.57%, 0.75%, and 0.48% on the models YOLOv3, the YOLOv4-tiny, and the YOLOv4, respectively.

原文English
頁(從 - 到)2722-2726
頁數5
期刊IEEE Signal Processing Letters
29
DOIs
出版狀態Published - 2022

All Science Journal Classification (ASJC) codes

  • 訊號處理
  • 應用數學
  • 電氣與電子工程

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