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http://dx.doi.org/10.14372/IEMEK.2022.17.5.273

Construction and Effectiveness Evaluation of Multi Camera Dataset Specialized for Autonomous Driving in Domestic Road Environment  

Lee, Jin-Hee (DGIST)
Lee, Jae-Keun (FutureDriveI)
Park, Jaehyeong (DGIST)
Kim, Je-Seok (DGIST)
Kwon, Soon (DGIST)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.17, no.5, 2022 , pp. 273-280 More about this Journal
Abstract
Along with the advancement of deep learning technology, securing high-quality dataset for verification of developed technology is emerging as an important issue, and developing robust deep learning models to the domestic road environment is focused by many research groups. Especially, unlike expressways and automobile-only roads, in the complex city driving environment, various dynamic objects such as motorbikes, electric kickboards, large buses/truck, freight cars, pedestrians, and traffic lights are mixed in city road. In this paper, we built our dataset through multi camera-based processing (collection, refinement, and annotation) including the various objects in the city road and estimated quality and validity of our dataset by using YOLO-based model in object detection. Then, quantitative evaluation of our dataset is performed by comparing with the public dataset and qualitative evaluation of it is performed by comparing with experiment results using open platform. We generated our 2D dataset based on annotation rules of KITTI/COCO dataset, and compared the performance with the public dataset using the evaluation rules of KITTI/COCO dataset. As a result of comparison with public dataset, our dataset shows about 3 to 53% higher performance and thus the effectiveness of our dataset was validated.
Keywords
2D Dataset; Camera; Autonomous driving;
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Times Cited By KSCI : 2  (Citation Analysis)
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