@inproceedings{1c001ef27b7b43a085ced4cf873403e9,
title = "D2D: Divide to Detect, A Scale-Aware Framework for On-Road Object Detection Using IR Camera",
abstract = "In this paper, to solve the problem of inconsistencies between the predictions in today's SOTA object detection networks, which incorporate the pyramid architecture with multi-level prediction, we proposed a scale-aware framework for IR image-based on-road object detection. The proposed framework uses scale-based attention mechanism to assign responsibilities to each feature levels. With this design, each feature level will focus on detecting a certain range of object scales, thereby minimizing the conflict among the predictions in the final result. Compared to Scaled-YOLOv4 baseline, our proposed method can achieve better performance without increasing FPS on FLIR dataset. The experimental results on RGB image-based object detection datasets also show that our proposed method gives good improvements when applied to RGB images.",
keywords = "Deep learning, IR Image, Object Detection",
author = "Luu, {Van Tin} and Tran, {Vu Hoang} and Egor Poliakov and Huang, {Ching Chun}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Consumer Electronics, ICCE 2023 ; Conference date: 06-01-2023 Through 08-01-2023",
year = "2023",
doi = "10.1109/ICCE56470.2023.10043569",
language = "English",
series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE International Conference on Consumer Electronics, ICCE 2023",
address = "United States",
}