Presentation information

Organized Session

Organized Session » OS-10

[1N4-OS-10a] System1型+2型統合AIへの展望(1/2)

Tue. Jun 14, 2022 2:20 PM - 4:00 PM Room N (Room 501)

オーガナイザ:栗原 聡(慶應義塾大学)[現地]、山川 宏(全脳アーキテクチャ・イニシアティブ)、三宅 陽一郎(スクウェア・エニックス)

2:40 PM - 3:00 PM

[1N4-OS-10a-02] Object Detection Using Scene Information

〇Yuya Osaki1, Yoshihiko Kato1, Yutaro Yamanaka1, Taro Tokui1, Satoshi Kurihara1 (1. Keio University)


Keywords:object detection, traffic light detection, active search, knowledge

Object detection in images plays an important role in assisting visually impaired people who have limited visual information to walk. In recent years, neural network-based inference has become the mainstream for object detection. However, due to the structure of object detectors, the input image is reduced to a fixed size, and thus the detection of small objects has been considered a difficult problem. In order to solve this problem, a method that divides the input image into segments and then performs inference on each segmented image is considered. However, this method increases the processing time significantly. In this study, we propose a method that creates a knowledge database similar to the one we have, and uses it to perform inference by focusing on areas where the target object is likely to be. In this study, we conducted experiments under the assumption that we have knowledge that there is a traffic light behind a pedestrian crossing, and showed that we can detect traffic lights that are not detected by inference based on the input image. We also showed that our system can reduce the processing time compared to a method that performs inference on a segmented input image.

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