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JACIII Vol.27 No.5 pp. 886-895
doi: 10.20965/jaciii.2023.p0886
(2023)

Research Paper:

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An Object Detection Method Using Probability Maps for Instance Segmentation to Mask Background

Shinji Uchinoura, Junichi Miyao, and Takio Kurita

Hiroshima University
1-4-1 Kagamiyama, Higashi-Hiroshima-shi, Hiroshima 739-8527, Japan

Received:
February 23, 2023
Accepted:
May 15, 2023
Published:
September 20, 2023
Creative Commons License
Keywords:
object detection, instance segmentation, deep neural networks
Abstract

This paper proposes a two-step detector called segmented object detection, whose performance is improved by masking the background region. Previous single-stage object detection methods suffer from the problem of imbalance between foreground and background classes, where the background occupies more regions in the image than the foreground. Thus, the loss from the background is firmly incorporated into the training. RetinaNet addresses this problem with Focal Loss, which focuses on foreground loss. Therefore, we propose a method that generates probability maps using instance segmentation in the first step and feeds back the generated maps as background masks in the second step as prior knowledge to reduce the influence of the background and enhance foreground training. We confirm that the detector can improve the accuracy by adding instance segmentation information to both the input and output rather than only to the output results. On the Cityscapes dataset, our method outperforms the state-of-the-art methods.

Overview of the proposed method

Overview of the proposed method

Cite this article as:
S. Uchinoura, J. Miyao, and T. Kurita, “An Object Detection Method Using Probability Maps for Instance Segmentation to Mask Background,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.5, pp. 886-895, 2023.
Data files:
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Last updated on Apr. 22, 2024