IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
권호 표지
DOI QR코드

DOI QR Code

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

국내 도로 환경에 특화된 자율주행을 위한 멀티카메라 데이터 셋 구축 및 유효성 검증

  • Received : 2022.07.12
  • Accepted : 2022.08.23
  • Published : 2022.10.31
Download PDF
⟨ Previous Next ⟩

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

Acknowledgement

본 연구는 과학기술정보통신부에서 지원하는 대구경북과학기술원 일반사업 (22-CoE-IT-01)과 기관고유사업 (22-IT-02) 및 지역현안 해결형 R&BD 사업 (2020-DD-RD-0339) 지원을 받아 수행 되었습니다.

References

  1. T. Y. LIN, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, C. L. Zitnick, "Microsoft COCO: Common Objects in Context," in Proc. European Conference on Computer Vision, Springer, Cham, pp. 740-755, 2014.
  2. A. Geiger, P. Lenz, C. Stiller, R. Urtasun, "Vision Meets Robotics: The Kitti Dataset," The International Journal of Robotics Research, Vol. 32, No. 11, pp. 1231-1237, 2013. https://doi.org/10.1177/0278364913491297
  3. P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, V. Vasudevan, W. Han, J. Ngiam, H. Zhao, A. Timofeev, S. Ettinger, M. Krivokon, A. Gao, A. Joshi, Y. Zhang, J. Shlens, Z. Chen, D. Anguelov, "Scalability in Perception for Autonomous Driving: Waymo Open Dataset," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 2446-2454, 2020.
  4. H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, O. Beijbom, "Nuscenes: A Multimodal Dataset for Autonomous Driving," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 11621-11631, 2020.
  5. A. Bochkovskiy, C. Y. Wang, H. Y. M. Liao, "Yolov4: Optimal Speed and Accuracy of Object Detection," arXiv preprint arXiv:2004.10934, 2020.
  6. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, A. C. Berg, "Ssd: Single Shot Multibox Detector," in Proc. European Conference on Computer Vision, Springer, Cham, pp. 21-37, 2016.
  7. T. Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollar. "Focal Loss for Dense Object Detection," in Proc. IEEE International Conference on Computer Vision, pp. 2980-2988, 2017.
  8. R. Girshick, J. Donahue, T. Darrell, J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, 2014.
  9. R. Girshick, "Fast r-cnn," Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448, 2015.
  10. S. Ren, K. He, R. Girshick, J. Sun, "Faster r-cnn: Towards Real-time Object Detection with Region Proposal Networks," Advances in Neural Information Processing Systems 28 2015.
  11. C. Y. Wang, H. Y. M. Liao, Y. H. Wu, P. Y. Chen, J. W. Hsieh, I. H. Yeh, "CSPNet: A New Backbone that can Enhance Learning Capability of CNN," in Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 390-391, 2020.
  12. G. Huang, Z. Liu, L. v. d. Maaten, K. Q. Weinberger, "Densely Connected Convolutional Networks," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700-4708, 2017.
  13. K. He, X. Zhang, S. Ren, J. Sun, "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 9, pp. 1904-1916, 2015. https://doi.org/10.1109/TPAMI.2015.2389824
  14. S. Liu, L. Qi, H. Qin, J. Shi, J. Jia, "Path Aggregation Network for Instance Segmentation," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759-8768, 2018.
  15. H. K. Kim, K. Y. Yoo, J. H. Park, H. Y. Jung, "Deep Learning Based Gray Image Generation from 3D LiDAR Reflection Intensity," IEMEK J. Embed. Sys. Appl., Vol. 14, No. 1, pp. 1-9, 2019 (in Korean).