基于自适应增强和动态特征融合的微光目标检测

IF 6.2 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Siyong Fu, Qinghua Zhao, Hesheng Liu, Qiuxiang Tao, Danjuan Liu
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引用次数: 0

摘要

在低光条件下,目标检测任务面临着低亮度、低对比度和噪声等挑战,这些挑战可能导致遗漏或错误的检测。针对这一问题,本文提出了一种低光增强算法DAMFCN,以及一种改进的DarkYOLOv8方法,旨在提高低光图像质量和目标检测性能。DAMFCN通过集成低光自适应模块和多尺度特征补偿块,显著提高了低光图像质量,其中LLAM有效提取精细细节并抑制噪声,MSFCB通过集成多尺度信息补偿丢失的细节。DarkYOLOv8框架建立在EfficientNet主干上,结合了多尺度注意机制和动态特征融合注意模块,在弱光条件下展示了卓越的目标检测性能。实验结果表明,该方法在准确性、鲁棒性和效率方面优于现有的先进技术,具有广阔的应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-light object detection via adaptive enhancement and dynamic feature fusion
Under low-light conditions, object detection tasks face challenges such as low brightness, low contrast, and noise, which can lead to missed or incorrect detections. To address this issue, this paper proposes a low-light enhancement algorithm, called DAMFCN, and an improved DarkYOLOv8 method, aimed at enhancing low-light image quality and object detection performance. DAMFCN significantly improves the quality of low-light images by integrating the Low-Light Adaptive Module and the Multi-Scale Feature Compensation Block, where LLAM effectively extracts fine details and suppresses noise, and MSFCB compensates for lost details by integrating multi-scale information. The DarkYOLOv8 framework, built on the EfficientNet backbone, combines a multi-scale attention mechanism and the Dynamic Feature Fusion Attention Module, demonstrating superior object detection performance under low-light conditions. Experimental results show that the proposed methods outperform existing state-of-the-art techniques in terms of accuracy, robustness, and efficiency, offering broad application potential.
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来源期刊
alexandria engineering journal
alexandria engineering journal Engineering-General Engineering
CiteScore
11.20
自引率
4.40%
发文量
1015
审稿时长
43 days
期刊介绍: Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification: • Mechanical, Production, Marine and Textile Engineering • Electrical Engineering, Computer Science and Nuclear Engineering • Civil and Architecture Engineering • Chemical Engineering and Applied Sciences • Environmental Engineering
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