A low light object detection method based on a modified YOLO11 model

In the field of computer vision, object detection in low-light scenarios remains an unresolved challenge. Traditional object-detection algorithms often encounter significant performance degradation under such conditions. This paper proposes a novel low light objective detection method based on a mod...

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Bibliographic Details
Published in:Data Driven Control and Learning Systems Conference (Online) pp. 2163 - 2168
Main Authors: Zhou, Meng, He, Shuke, Wang, Jing, Wang, Chang
Format: Conference Proceeding
Language:English
Published: IEEE 09.05.2025
Subjects:
CBAM-YOLO11
Control systems
Data models
Degradation
Feature extraction
Image color analysis
Image enhancement
Image quality
Learning systems
Lighting
Low-light object detection
Object detection
SCI module
ISSN:2767-9861
Online Access:Get full text
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Summary:In the field of computer vision, object detection in low-light scenarios remains an unresolved challenge. Traditional object-detection algorithms often encounter significant performance degradation under such conditions. This paper proposes a novel low light objective detection method based on a modified YOLO11 model. First, to improve the image quality in terms of brightness, contrast, and color fidelity, the self-calibrated illumination (SCI) module is utilized to enhance low-light images. Then, to enhance the model's ability to capture key object features, a CBAM-YOLO11 model is presented by integrating the convolutional block attention module (CBAM) into YOLO11 model, Finally, the proposed method is illustrated by EXdark dataset for low-light object detection. The results show that the proposed approach surpasses other state-of-the-art methods in detecting objects under low-light conditions. The mean average precision has been significantly improved, along with notable enhancements in recall and precision rates.
ISSN:2767-9861
DOI:10.1109/DDCLS66240.2025.11065251