| 基于深度空时域特征融合的高动态空中多形态目标检测方法(特邀) |
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| 引用本文： | 孙鹏,于跃,陈嘉欣,秦翰林.基于深度空时域特征融合的高动态空中多形态目标检测方法(特邀)[J].红外与激光工程,2022,51(4):20220167-1-20220167-8. |
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| 作者姓名： | 孙鹏 于跃 陈嘉欣 秦翰林 |
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| 作者单位： | 西安电子科技大学 光电工程学院，陕西 西安 710071 |
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| 基金项目： | 国家自然科学基金(62174128) |
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| 摘 要： | 针对复杂背景下，依靠高超声速飞行器搭载的红外探测器对高动态空中目标的可靠探测和精确识别问题，提出了一种基于深度空时域特征融合的空中多形态目标检测方法。设计了加权双向循环特征金字塔结构提取多形态目标静态特征，并引入可切换空洞卷积，增大感受野的同时减少空域信息损失。对于时序运动特征的提取，为了抑制复杂背景噪声的同时将角点信息集中到运动区域中，通过特征点匹配法生成掩膜图，之后进行光流计算，根据计算结果设计稀疏光流特征图，利用3D卷积提取多个连续帧图像中包含的时序特征，生成三维时序运动特征图。最后，通过对图像静态特征与时序运动特征进行通道维度的拼接，实现深度空时域特征融合。大量的对比实验表明，文中方法可明显减少复杂背景下的虚假识别概率，具备高实时性的同时目标识别准确率达89.87%，满足高动态下的红外目标智能检测识别需求。
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| 关 键 词： | 目标检测 特征融合 多尺度金字塔 稀疏光流 3D卷积 |
| 收稿时间： | 2022-03-10 |
Highly dynamic aerial polymorphic target detection method based on deep spatial-temporal feature fusion (Invited) |
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| Affiliation: | School of Optoelectronic Engineering, Xidian University, Xi'an 710071, China |
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| Abstract: | Aiming at the problem of reliable detection and accurate recognition of high dynamic aerial targets by infrared detectors carried by hypersonic vehicles in complex background, an aerial polymorphic target detection method based on deep spatial-temporal feature fusion was proposed. A weighted bidirectional cyclic feature pyramid structure was designed to extract the static features of polymorphic target, and switchable atrous convolution was introduced to increase the receptive field and reduce spatial information loss. For the extraction of temporal motion features, in order to suppress the complex background noise and concentrate the corner information into the moving region, the feature point matching method was used to generate the mask image, then the optical flow was calculated, and the sparse optical flow feature map was designed according to calculation results. Finally, the temporal features contained in multiple continuous frame images were extracted by 3D convolution to generate a 3D temporal motion feature map. By concatting the image static features and temporal motion features in channel dimension, the deep spatial-temporal fusion could be realized. A large number of comparative experiments showed that this method can significantly reduce the false recognition probability in complex background, and the target detection accuracy reached 89.87% with high real-time performance, which can meet the needs of infrared targets intelligent detection and recognition under high dynamic conditions. |
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