Search by item HOME > Access full text > Search by item

JBE, vol. 26, no. 2, pp.155-166, March, 2021


Performance Analysis of Object Detection Neural Network According to Compression Ratio of RGB and IR Images

Yegi Lee, Shin Kim, Hanshin Lim, Hee Kyung Lee, Hyon-Gon Choo, Jeongil Seo, and Kyoungro Yoon

C.A E-mail:


Most object detection algorithms are studied based on RGB images. Because the RGB cameras are capturing images based on light, however, the object detection performance is poor when the light condition is not good, e.g., at night or foggy days. On the other hand, high-quality infrared(IR) images regardless of weather condition and light can be acquired because IR images are captured by an IR sensor that makes images with heat information. In this paper, we performed the object detection algorithm based on the compression ratio in RGB and IR images to show the detection capabilities. We selected RGB and IR images that were taken at night from the Free FLIR Thermal[1] dataset for the ADAS(Advanced Driver Assistance Systems) research. We used the pre-trained object detection network for RGB images and a fine-tuned network that is tuned based on night RGB and IR images. Experimental results show that higher object detection performance can be acquired using IR images than using RGB images in both networks.

Keyword: RGB image, IR image, object detection, deep learning, fine-tuned network

[1] Free FLIR Thermal dataset, (accessed Jan, 8, 2020).
[2] Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580-587, 2014.
[3] Girshick, Ross. "Fast r-cnn," Proceedings of the IEEE international conference on computer vision, pp. 1440-1448, 2015.
[4] Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal networks," IEEE transactions on pattern analysis and machine intelligence, Vol. 39, No. 6, pp. 1137-1149, 2016.
[5] Redmon, Joseph, et al. "You only look once: Unified, real-time object detection," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788, 2016.
[6] Lin, Tsung-Yi, et al. "Focal loss for dense object detection." Proceedings of the IEEE international conference on computer vision, pp. 2980-2988, 2017.
[7] ImageNet dataset, (accessed Jan, 8, 2020).
[8] coco dataset, (accessed Jan, 8, 2020).
[9] Liu, Shuo, and Zheng Liu. "Multi-channel CNN-based object detection for enhanced situation awareness." arXiv preprint arXiv:1712. 00075 (2017).
[10] Farahnakian, Fahimeh, et al. "Deep Convolutional Neural Network- based Fusion of RGB and IR Images in Marine Environment," 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 21-26, 2019.
[11] Y.-H. Chao, Y.-C. Sun, J. Xu, X. Xu, “JVET common testconditions and software reference configurations for non4:2:0 colour formats,” document JVET -R2013, April. 2020.
[12] detectron2, (accessed Jan, 8, 2020).
[13] AP[0.5:0.95], (accessed Jan, 8, 2020).


Editorial Office
1108, New building, 22, Teheran-ro 7-gil, Gangnam-gu, Seoul, Korea
Homepage: TEL: +82-2-568-3556 FAX: +82-2-568-3557
Copyrightⓒ 2012 The Korean Institute of Broadcast and Media Engineers
All Rights Reserved