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Color Channel Transformation Enhancement-based Low-illumination Images Object Detection
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Min ZHANG1, 2, Wensheng QIAO1, 2, Peipei ZHU1, 2, Sihan ZHU1, 2, Yufei ZHAN1, 2, Xiaochen HUANG3, Honggang CHEN3
Telecommunication Engineering | 2025, 65(11) : 1781 - 1788
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Telecommunication Engineering | 2025, 65(11): 1781-1788
• Application Fundamental Research and Advanced Technology •
Color Channel Transformation Enhancement-based Low-illumination Images Object Detection
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Min ZHANG1, 2, Wensheng QIAO1, 2, Peipei ZHU1, 2, Sihan ZHU1, 2, Yufei ZHAN1, 2, Xiaochen HUANG3, Honggang CHEN3
Affiliations
  • 1Southwest China Institute of Electronic Technology，Chengdu 610036，China
  • 2National Key Laboratory of Complex Aviation System Simulation，Chengdu 610036，China
  • 3College of Electronics and Information Engineering，Sichuan University，Chengdu 610065，China
Published: 2025-11-28 doi: 10.20079/j.issn.1001-893x.250506001
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Object detection technology aims to locate and identify specific category targets in images or videos. However，in low-illumination scenarios，problems such as low contrast，blurred boundaries，and noise interference，result in the decline of detection performance. To address this，a Color Channel Transformation Enhancement-based Object Detection (C2TEOD ) algorithm is proposed. Firstly，a color channel transformation module is constructed，and learnable parameters are introduced to transform different color channels，enhancing the flexibility of the enhancement strategy. Then，an image enhancement module is employed to preprocess the input images. This module is jointly optimized with the object detection network using detection loss functions，thereby enabling the enhancement module to learn to generate representations that explicitly facilitate the subsequent detection task. Additionally，a selective self-supervised regression loss is proposed that uses both the original low-illumination images and the enhanced images as inputs to optimize the detection network. According to detection results，the enhancement module is further optimized through self-supervised regression to improve detection performance. Experimental results show that，compared with the baseline method，the mean average precision(mAP) metrics on the Exdark，M3FD，and LLVIP datasets are improved by 2.2%，1.1%，and 0.2% respectively.

object detection  /  image enhancement  /  deep learning  /  joint optimization  /  self-supervision
Min ZHANG, Wensheng QIAO, Peipei ZHU, Sihan ZHU, Yufei ZHAN, Xiaochen HUANG, Honggang CHEN. Color Channel Transformation Enhancement-based Low-illumination Images Object Detection[J]. Telecommunication Engineering, 2025 , 65 (11) : 1781 -1788 . DOI: 10.20079/j.issn.1001-893x.250506001
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Year 2025 volume 65 Issue 11
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doi: 10.20079/j.issn.1001-893x.250506001
  • Receive Date：2025-05-06
  • Online Date：2026-04-15
  • Published：2025-11-28
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  • Received：2025-05-06
  • Revised：2025-08-29
Affiliations
    1Southwest China Institute of Electronic Technology，Chengdu 610036，China
    2National Key Laboratory of Complex Aviation System Simulation，Chengdu 610036，China
    3College of Electronics and Information Engineering，Sichuan University，Chengdu 610065，China
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