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

DOI: https://doi.org/10.5909/JBE.2021.26.2.155

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: yoonk@konkuk.ac.kr

Abstract:

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

Reference:
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