Logo PTI Logo FedCSIS

Position Papers of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 31

New Thermal Automotive Dataset for Object Detection

, ,

DOI: http://dx.doi.org/10.15439/2022F283

Citation: Position Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 31, pages 4348 ()

Full text

Abstract. Although there are many efficient deep learningmethods, object detection and classification in visible spectrum have many limitations especially in case of poor light conditions. To fill this gap, we created a novel thermal video database containing few thousands of frames with annotated objects acquired in far infrared thermal spectrum. Thanks to this we were able to show its usability in the traffic object recognition based on the YOLOv5 network, properly trained to gain maximal performance on thermal images, which contain many small objects and are characteristic of different properties than the visible spectrum counterparts. The proposed thermal database, as well as the fully trained model are main contributions of this paper. These are made available free for other researchers. Additionally, based on the highly efficient car detector we show its application in the car speed measurement based exclusively on thermal images. The proposed system can be also used in the Advanced DriverAssistance Systems (ADAS), and help autonomous driving.


  1. J. M. Lloyd, Thermal imaging systems. Springer Science & Business Media, 2013.
  2. M. Krišto, M. Ivasic-Kos, and M. Pobar, “Thermal object detection in difficult weather conditions using yolo,” IEEE access, vol. 8, pp. 125 459–125 476, 2020.
  3. J. Gąsienica-Józkowy, M. Knapik, and B. Cyganek, “An ensemble deep learning method with optimized weights for drone-based water rescue and surveillance,” Integrated Computer-Aided Engineering, vol. 28, pp. 221–235, 2021, 3.
  4. M. Knapik and B. Cyganek, “Fast eyes detection in thermal images,” Multimedia Tools and Applications, vol. 80, no. 3, pp. 3601–3621, 2021.
  5. J. Redmon and A. Farhadi, “Yolo9000: Better, faster, stronger,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525, 2017.
  6. J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” ArXiv, vol. abs/1804.02767, 2018.
  7. G. Jocher, A. Chaurasia, A. Stoken, J. Borovec, NanoCode012, Y. Kwon, TaoXie, J. Fang, imyhxy, K. Michael, Lorna, A. V, D. Montes, J. Nadar, Laughing, tkianai, yxNONG, P. Skalski, Z. Wang, A. Hogan, C. Fati, L. Mammana, AlexWang1900, D. Patel, D. Yiwei, F. You, J. Hajek, L. Diaconu, and M. T. Minh, “ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference,” Feb. 2022. [Online]. Available: https://doi.org/10.5281/zenodo.6222936
  8. M. Bhattarai and M. Martinez-Ramon, “A deep learning framework for detection of targets in thermal images to improve firefighting,” IEEE Access, vol. 8, pp. 88 308–88 321, 2020.
  9. J. Gong, J. Zhao, F. Li, and H. Zhang, “Vehicle detection in thermal images with an improved yolov3-tiny,” in 2020 IEEE international conference on power, intelligent computing and systems (ICPICS). IEEE, 2020, pp. 253–256.
  10. W. Zhou, Q. Guo, J. Lei, L. Yu, and J.-N. Hwang, “Ecffnet: Effective and consistent feature fusion network for rgb-t salient object detection,” IEEE Transactions on Circuits and Systems for Video Technology, 2021.
  11. X. Dai, X. Yuan, and X. Wei, “Tirnet: Object detection in thermal infrared images for autonomous driving,” Applied Intelligence, vol. 51, no. 3, pp. 1244–1261, 2021.
  12. R. E. Rivadeneira, A. D. Sappa, and B. X. Vintimilla, “Thermal image super-resolution: A novel architecture and dataset.” in VISIGRAPP (4: VISAPP), 2020, pp. 111–119.
  13. S. R. Yeduri, D. S. Breland, S. B. Skriubakken, O. J. Pandey, and L. R. Cenkeramaddi, “Low resolution thermal imaging dataset of sign language digits,” Data in Brief, vol. 41, p. 107977, 2022.
  14. M. Knapik and B. Cyganek, “Driver’s fatigue recognition based on yawn detection in thermal images,” Neurocomputing, vol. 338, pp. 274–292, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231219302280
  15. M. A. Farooq, P. Corcoran, C. Rotariu, and W. Shariff, “Object detection in thermal spectrum for advanced driver-assistance systems (adas),” IEEE Access, vol. 9, pp. 156 465–156 481, 2021.
  16. “Darklabel annotation software,” https://github.com/darkpgmr/DarkLabel, accessed: 2022-06-07.
  17. “Dz. u. 26.11.2019, position 2311, szczegółowe warunki techniczne dla znaków drogowych poziomych i warunki ich umieszczania na drogach, section,” https://sip.lex.pl/akty-prawne/dzu-dziennik-ustaw/szczegolowe-warunki-techniczne-dla-znakow-i-sygnalow-drogowych-oraz-17066287, accessed: 2022-06-07.