research-article

A Dark Transformation-Equivariant Algorithm for Dark Object Detection

Authors:

Guoheng HuangAuthors Info & Claims

CNIOT '23: Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things

Pages 731 - 735

https://doi.org/10.1145/3603781.3603911

Published: 27 July 2023 Publication History

Abstract

With the advent of deep learning, convolution neural networks (CNN) based object detection plays a critical role in fields such as autonomous driving. Unlike the normal-lit scenario, the dark environment can be challenging for object detection due to the low lighting and dark noise. To improve object detection in low-light conditions, we introduce a novel Dark Transformation-Equivariant (DTE) algorithm to explore feature consistency between the normal-lit and low-light domains. Specifically, on the one hand, we construct a dark transformation to simulate poor lighting conditions by darkening the regular images, considering the sensor noise. On the other hand, we capture the representation invariance of the dark transformation by encouraging feature consistency. Under the design of DTE, detectors are able to learn more discriminative representations of dark images for low-light object detection. Experiments on the ExDark dataset demonstrate the effectiveness of our method in improving detection performance when suffering from a dark environment and we achieve a 3.4% higher mAP than that of YOLOv3.

References

[1]

Mahsa Arabahmadi, Reza Farahbakhsh, and Javad Rezazadeh. 2022. Deep learning for smart Healthcare—A survey on brain tumor detection from medical imaging. Sensors 22, 5 (2022), 1960.

[2]

Long Chen, Shaobo Lin, Xiankai Lu, Dongpu Cao, Hangbin Wu, Chi Guo, Chun Liu, and Fei-Yue Wang. 2021. Deep neural network based vehicle and pedestrian detection for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems 22, 6 (2021), 3234–3246.

[3]

Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597–1607.

[4]

Xinlei Chen and Kaiming He. 2021. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 15750–15758.

[5]

Ziteng Cui, Guo-Jun Qi, Lin Gu, Shaodi You, Zenghui Zhang, and Tatsuya Harada. 2021. Multitask aet with orthogonal tangent regularity for dark object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 2553–2562.

[6]

Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. 2010. The pascal visual object classes (voc) challenge. International journal of computer vision 88, 2 (2010), 303–338.

Digital Library

[7]

Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, 2020. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems 33 (2020), 21271–21284.

[8]

Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. 2020. Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1780–1789.

[9]

Xiaojie Guo, Yu Li, and Haibin Ling. 2016. LIME: Low-light image enhancement via illumination map estimation. IEEE Transactions on image processing 26, 2 (2016), 982–993.

Digital Library

[10]

Haidi Ibrahim and Nicholas Sia Pik Kong. 2007. Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics 53, 4 (2007), 1752–1758.

Digital Library

[11]

Sanket Kalwar, Dhruv Patel, Aakash Aanegola, Krishna Reddy Konda, Sourav Garg, and K Madhava Krishna. 2022. GDIP: Gated Differentiable Image Processing for Object-Detection in Adverse Conditions. arXiv preprint arXiv:2209.14922 (2022).

[12]

Tero Karras, Samuli Laine, and Timo Aila. 2019. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 4401–4410.

[13]

Chulwoo Lee, Chul Lee, and Chang-Su Kim. 2013. Contrast enhancement based on layered difference representation of 2D histograms. IEEE transactions on image processing 22, 12 (2013), 5372–5384.

Digital Library

[14]

Tong Liu, Zhaowei Chen, Yi Yang, Zehao Wu, and Haowei Li. 2020. Lane detection in low-light conditions using an efficient data enhancement: Light conditions style transfer. In 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, 1394–1399.

Digital Library

[15]

Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, Jianke Zhu, and Lei Zhang. 2022. Image-adaptive YOLO for object detection in adverse weather conditions. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 1792–1800.

[16]

Yuen Peng Loh and Chee Seng Chan. 2019. Getting to know low-light images with the exclusively dark dataset. Computer Vision and Image Understanding 178 (2019), 30–42.

[17]

Feifan Lv, Feng Lu, Jianhua Wu, and Chongsoon Lim. 2018. MBLLEN: Low-Light Image/Video Enhancement Using CNNs. In BMVC, Vol. 220. 4.

[18]

Guo-Jun Qi, Liheng Zhang, Chang Wen Chen, and Qi Tian. 2019. Avt: Unsupervised learning of transformation equivariant representations by autoencoding variational transformations. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 8130–8139.

[19]

Zia-ur Rahman, Daniel J Jobson, and Glenn A Woodell. 2004. Retinex processing for automatic image enhancement. Journal of Electronic imaging 13, 1 (2004), 100–110.

[20]

Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018).

[21]

Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015).

[22]

Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. 2018. Deep retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560 (2018).

[23]

Liheng Zhang, Guo-Jun Qi, Liqiang Wang, and Jiebo Luo. 2019. Aet vs. aed: Unsupervised representation learning by auto-encoding transformations rather than data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2547–2555.

[24]

Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision. 2223–2232.

Cited By

Wang XYang RWu ZSun LLiu JZou L(2025)ILENet: Illumination-Modulated Laplacian-Pyramid Enhancement Network for low-light object detectionExpert Systems with Applications10.1016/j.eswa.2025.126504271(126504)Online publication date: May-2025
https://doi.org/10.1016/j.eswa.2025.126504

Index Terms

A Dark Transformation-Equivariant Algorithm for Dark Object Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

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CNIOT '23: Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things

May 2023

1025 pages

ISBN:9798400700705

DOI:10.1145/3603781

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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New York, NY, United States

Publication History

Published: 27 July 2023

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CNIOT'23

CNIOT'23: 2023 4th International Conference on Computing, Networks and Internet of Things

May 26 - 28, 2023

Xiamen, China

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Cited By

Wang XYang RWu ZSun LLiu JZou L(2025)ILENet: Illumination-Modulated Laplacian-Pyramid Enhancement Network for low-light object detectionExpert Systems with Applications10.1016/j.eswa.2025.126504271(126504)Online publication date: May-2025
https://doi.org/10.1016/j.eswa.2025.126504

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