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一种自标签特征点异源图像目标检测算法

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一种自标签特征点异源图像目标检测算法
A Self-Labeling Feature Matching Algorithm for Instance Recognition on Multi-Sensor Images
投稿时间：2020-07-23
DOI：10.15918/j.tbit1001-0645.2020.092
中文关键词:实例检测异源图像特征匹配自标签深度学习
English Keywords:instance recognitionmulti-sensor imagesfeature matchingself-labelingdeep learning
基金项目:中国青年科学基金资助项目（61702413）
作者单位
张栩培西安微电子技术研究所, 陕西, 西安 710068
贺占庄西安微电子技术研究所, 陕西, 西安 710068
马钟西安微电子技术研究所, 陕西, 西安 710068
杨一岱西安微电子技术研究所, 陕西, 西安 710068
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中文摘要:
由于图像成像机理差别较大，现有的算法无法提取可见光与红外异源图像上的共有特征用来匹配，进而无法实现异源图像目标识别. 针对此问题，本文提出了一种基于自标签技术的深度学习特征点提取匹配算法. 算法通过设计一个粗特征检测器并在合成影像上进行训练，使得该特征检测器在不同图像上都具特征提取能力. 利用本文提出的自标签方法将异源图像中共有的特征点进行提取，从而解决了现有算法无法获取异源图像共有特征的问题. 并利用自标签结果进行特征点检测器和描述子的训练，最终通过匹配的特征点实现了异源图像间的实例目标识别. 本文采集了不同场景下的可见光-红外无人机影像作为测试数据. 在异源测试数据集上，选择了6种不同的先进算法与本文算法进行了对比试验. 实验结果表明，该算法较现有的6种先进算法能够提取到更多、更精确的异源图像共有特征，与其他测试算法相比在异源图像测试数据上的平均精度有了明显提升.
English Summary:
Due to the great difference of the images from imaging apparatuses, the existing state-of-the-art algorithms are hard to obtain corresponding features for matching processing or the instance recognition on multi-sensor images (such as RGB and infrared images). To solve this problem, a new feature matching algorithm was proposed based on a self-labeling technique, being able deep learning and extracting feature. Firstly, an immature detector was designed and trained with synthetic images to be competent for feature extraction on different types of images. Then, a self-labeling method was proposed to obtain the corresponding feature points on the multi sensors images, and the self-labeling results were used for detector and descriptor training to achieving the instance recognition on multi-sensor images based on the matching feature points. Finally, hundreds of pairs RGB and Infrared images were collected from different scenarios and condition, and some experiments were carried out to compare the proposed algorithm with 6 different state-of-the-art algorithms. The experiment results show that the proposed algorithm can provide much and accurate corresponding feature points than other state-of-the-art algorithms, improving the average precision significantly.
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