| 引用本文: | 牛轶峰,朱宇亭,李宏男,等.基于部件模型的无人机系统小样本车辆目标识别方法.[J].国防科技大学学报,2021,43(1):117-126.[点击复制] |
| NIU Yifeng,ZHU Yuting,LI Hongnan,et al.Small sample vehicle target recognition using component model for unmanned aerial vehicle[J].Journal of National University of Defense Technology,2021,43(1):117-126[点击复制] |
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| 基于部件模型的无人机系统小样本车辆目标识别方法 |
| 牛轶峰,朱宇亭,李宏男,王菖,吴立珍 |
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(国防科技大学 智能科学学院, 湖南 长沙 410073)
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| 摘要: |
| 对地目标检测与识别是无人机系统典型任务之一,但受限于任务特殊性,往往难以获取足够的目标样本数据以实现高可靠的目标识别。为此,结合人的认知特性,提出一种基于部件模型的小样本车辆目标识别方法,可有效提高无人机感知能力。采用视觉显著性检测与物体性检测相结合的检测方法,提取目标可能区域；采用基于图论的GrabCut方法与最大类间方差法相结合的分割方法,分割目标并提取目标内部件；采用基于概率图模型的部件识别方法,通过将部件轮廓稀疏表示为条件随机场,并进行概率推理实现部件识别；采用基于贝叶斯的目标识别方法完成目标是否为车辆的判断。通过无人机拍摄的车辆图像验证表明,算法可在样本较少、光照变化、存在遮挡等情况下,以较高准确率检测并识别出车辆目标,同时识别算法具有一定可解释性。 |
| 关键词: 无人机 目标识别 图像分割 部件模型 小样本学习 |
| DOI：10.11887/j.cn.202101016 |
| 投稿日期：2019-06-10 |
| 基金项目:国家自然科学基金资助项目(61876187) |
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| Small sample vehicle target recognition using component model for unmanned aerial vehicle |
| NIU Yifeng, ZHU Yuting, LI Hongnan, WANG Chang, WU Lizhen |
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(College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| Detecting and recognizing targets on the ground is one of the typical tasks of UAVs (unmanned aerial vehicles), but it is limited by the task particularity so that it is often difficult to obtain sufficient data about target samples to achieve highly reliable target recognition. In view of this problem, a small-sample vehicle target recognition method based on the component model was proposed, which combined the cognitive characteristics of human beings to improve the perception ability of ground targets. The possible region of the target was extracted by visual saliency detection and objectness detection, and then the GrabCut segmentation method based on the Graph theory and the maximum between-class variance was used to segment the target and to extract the components from the target. A component recognition method based on a probability map model was used to perform component recognition by sparsely representing a component outline as a conditional random field and performing probabilistic reasoning. The Bayesian-based target recognition method was used to determine whether the target was a vehicle. Verification on real images captured by the UAV showed that the algorithm can detect and identify the vehicle target with high accuracy under the condition of fewer samples, poorer illumination and certain occlusion. At the same time, the recognition method can achieve the effect of certain interpretability. |
| Keywords: unmanned aerial vehicle target recognition image segmentation component model small sample learning |
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