JRM Vol.32 No.6 pp. 1200-1210
doi: 10.20965/jrm.2020.p1200

Development Report:

Garbage Detection Using YOLOv3 in Nakanoshima Challenge

Jingwei Xue, Zehao Li, Masahito Fukuda, Tomokazu Takahashi, Masato Suzuki, Yasushi Mae, Yasuhiko Arai, and Seiji Aoyagi

Kansai University
3-3-35 Yamate-cho, Suita, Osaka 564-8680, Japan

July 7, 2020
October 20, 2020
December 20, 2020
deep learning, object detector
Garbage Detection Using YOLOv3 in Nakanoshima Challenge

Detected objects by recognition system with YOLOv3

Object detectors using deep learning are currently used in various situations, including robot demonstration experiments, owing to their high accuracy. However, there are some problems in the creation of training data, such as the fact that a lot of labor is required for human annotations, and the method of providing training data needs to be carefully considered because the recognition accuracy decreases due to environmental changes such as lighting. In the Nakanoshima Challenge, an autonomous mobile robot competition, it is challenging to detect three types of garbage with red labels. In this study, we developed a garbage detector by semi-automating the annotation process through detection of labels using colors and by preparing training data by changing the lighting conditions in three ways depending on the brightness. We evaluated the recognition accuracy on the university campus and addressed the challenge of using the discriminator in the competition. In this paper, we report these results.

Cite this article as:
Jingwei Xue, Zehao Li, Masahito Fukuda, Tomokazu Takahashi, Masato Suzuki, Yasushi Mae, Yasuhiko Arai, and Seiji Aoyagi, “Garbage Detection Using YOLOv3 in Nakanoshima Challenge,” J. Robot. Mechatron., Vol.32, No.6, pp. 1200-1210, 2020.
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Last updated on Feb. 25, 2021