| 一种基于SVM候选区训练的红外舰船目标检测方法 |
| 投稿时间：2019-03-13 修订日期：2019-03-19 点此下载全文 |
| 引用本文：修炳楠,吕俊伟,鹿珂珂.一种基于SVM候选区训练的红外舰船目标检测方法[J].红外,2019,40(3):16~23 |
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| 中文摘要:无人机进行红外舰船目标侦察时，检测算法对检测正确率的影响很大。为增强无人机红外光电载荷对舰船目标的检测能力，提出采用支持向量机(Support Vector Machine, SVM)进行候选区训练的检测算法，以提高目标检测的正确率。通过预先对候选区的特征进行训练，得到候选区的分类数据。在检测阶段，加载训练时得到的候选区分类数据，分类筛选出更可能包含目标的候选区，从而提高目标检测的正确率。验证实验中，选用368张无人机拍摄的长波红外图像作为训练数据集图像，另外选择139张图像作为测试图像。分别采用带候选区训练的方法和无候选区训练的方法做目标检测实验。检测结果表明，采用带候选区训练的检测方法比采用无候选区训练方法时平均检测正确率高14.6%。 |
| 中文关键词:舰船目标检测 候选区训练 SVM 选择性搜索算法 |
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| An Infrared Ship Target Detection Method Based on Region Proposal Training by SVM |
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| Abstract:When the unmanned aerial vehicle (UAV) performs infrared ship target reconnaissance, the detection algorithm has a great influence on the detection accuracy. In order to enhance the detection capability of the UAV′s infrared photoelectric load on the ship''s target, a support vector machine was used to detect the region proposal training algorithm to improve the accuracy of target detection. Region proposal classification data were obtained by training region features in advance. In the detection stage, the region classification data obtained by training was loaded, and the regions which were more likely to contain targets were selected, thereby improving the accuracy of the target detection. In the validation experiment, 368 long-wave infrared images captured by UAV were selected as data sets, and 139 infrared images were selected as test sets. The target detection experiments were carried out using the region proposal training method and non-region proposal training method. The detection results show that the mean accuracy is 14.6% higher when using the region proposal training method than when using the non-region proposal training method. |
| keywords:ship target detection region proposal training SVM selective search algorithm |
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