红外技术2024，Vol.46Issue(5)：548-555,8.
基于对比学习的改进SSD目标检测算法
Improved SSD Object Detection Algorithm Based on Contrastive Learning
摘要
Abstract
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%.关键词
深度学习/SSD/目标检测/对比学习/红外热成像/图像截块Key words
deep learning/SSD/object detection/contrastive learning/infrared thermal/image cropping分类
信息技术与安全科学引用本文复制引用
胡焱,原子昊,涂晓光,刘建华,雷霞,王文敬..基于对比学习的改进SSD目标检测算法[J].红外技术,2024,46(5):548-555,8.基金项目
中国博士后科学基金(2022M722248) （2022M722248）
四川省无人系统智能采集控制技术工程实验室开放课题(WRXT2021-001) （WRXT2021-001）
民航飞行技术飞行与安全重点实验室开放项目资助(FZ2022KF06) （FZ2022KF06）
中国民用航空飞行学院面上项目(J2023-026) （J2023-026）
中央高校基本科研业务费(ZHMH2022-004,J2022-025). （ZHMH2022-004,J2022-025）
