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基于对比学习的改进SSD目标检测算法

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
现有基于深度学习的目标检测算法在图像的目标检测过程中存在物体视角的多样性、目标本身形变、检测物体受遮挡、光照性以及小目标检测等问题.为了解决这些问题,本文将对比学习思想引入到SSD(Single Shot MutiBox Detectior)目标检测网络中,对原有的SSD算法进行改进.首先,通过采用图像截块的方式随机截取样本图片中的目标图片与背景图片,将目标图像块与背景图像块输入到对比学习网络中提取图片特征进行对比损失计算.随后,使用监督学习的方法对 SSD 网络进行训练,将对比损失传入到SSD网络中与SSD损失值加权求和反馈给SSD网络,进行网络参数的优化.由于在目标检测网络中加入了对比学习的思想,提高了背景和目标在特征空间中的区分度.因此所提出的算法能显著提高 SSD 网络对于目标检测的精度,并在可见光和热红外图像中均取得了令人满意的检测效果.在PASCAL VOC2012 数据集实验中,AP50值提升了 0.3%,在LLVIP数据集实验中,AP50值提升了 0.2%.
Improved SSD Object Detection Algorithm Based on Contrastive Learning
The existing deep learning-based object detection algorithms encounter various issues during the object detection process in images,such as object viewpoint diversity,object deformation,detection occlusion,illumination variations,and detection of small objects.To address these issues,this paper introduces the concept of contrastive learning into the SSD object detection network and improves the original SSD algorithm.First,by randomly cropping object images and background images from sample images using the method of image cropping,the object image blocks and background image blocks are input into the contrastive learning network for feature extraction and contrastive loss calculation.The supervised learning method is then used to train the SSD network,and the contrastive loss is fed into the SSD network and weighted and summed with the SSD loss value for feedback to optimize the network parameters.Because the contrastive learning concept is introduced into the object detection network,the distinction between the background and object in the feature space is improved.Therefore,the proposed algorithm significantly improves the accuracy of the SSD network for object detection,and obtains satisfactory detection results in both visible and thermal infrared images.In the experiment on the PASCAL VOC2012 dataset,the proposed algorithm shows an increase in the AP50 value by 0.3%,whereas in the case of the LLVIP dataset,the corresponding increase in AP50 value is 0.2%.

deep learningSSDobject detectioncontrastive learninginfrared thermalimage cropping

胡焱、原子昊、涂晓光、刘建华、雷霞、王文敬

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中国民用航空飞行学院 航空电子电气学院,四川 广汉 618307

深度学习 SSD 目标检测 对比学习 红外热成像 图像截块

中国博士后科学基金四川省无人系统智能采集控制技术工程实验室开放基金民航飞行技术飞行与安全重点实验室开放基金中国民用航空飞行学院面上项目中央高校基本科研业务费专项中央高校基本科研业务费专项

2022M722248WRXT2021-001FZ2022KF06J2023-026ZHMH2022-004J2022-025

2024

红外技术
昆明物理研究所 中国兵工学会夜视技术专业委员会

红外技术

CSTPCD北大核心
影响因子：0.914
ISSN：1001-8891
年,卷(期)：2024.46(5)
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