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http://dx.doi.org/10.12815/kits.2018.17.3.98

A Study on Improvement of Dynamic Object Detection using Dense Grid Model and Anchor Model  

Yun, Borin (Dept. of Computer Science and Engineering, Univ. of Inha)
Lee, Sun Woo (Dept. of Computer Science and Engineering, Univ. of Inha)
Choi, Ho Kyung (Dept. of Information and Electronic Engineering, Univ. of Mokpo)
Lee, Sangmin (Dept. of Electronic Engineering, Univ. of Inha)
Kwon, Jang Woo (Dept. of Computer Science and Engineering, Univ. of Inha)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.17, no.3, 2018 , pp. 98-110 More about this Journal
Abstract
In this paper, we propose both Dense grid model and Anchor model to improve the recognition rate of dynamic objects. Two experiments are conducted to study the performance of two proposed CNNs models (Dense grid model and Anchor model), which are to detect dynamic objects. In the first experiment, YOLO-v2 network is adjusted, and then fine-tuned on KITTI datasets. The Dense grid model and Anchor model are then compared with YOLO-v2. Regarding to the evaluation, the two models outperform YOLO-v2 from 6.26% to 10.99% on car detection at different difficulty levels. In the second experiment, this paper conducted further training of the models on a new dataset. The two models outperform YOLO-v2 up to 22.40% on car detection at different difficulty levels.
Keywords
CNNs; Next generation ITS; Safety Service; Object Detection; BVI pedestrian;
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