基于新型联合感知矩阵的压缩学习目标识别技术

Target Recognition Technology Based on a New Joint Sensing Matrix for Compressed Learning

目标识别正逐渐成为自动化领域中提供准确目标类别信息的一项重要技术,并且当前大多数目标识别方法都是基于深度学习框架实现.通常,深度学习框架的输入数据均为原始图像数据,而在实际应用中,探测器获取原始图像数据并作为深度学习框架的输入进而实现目标识别的方式并非是高效的,数据获取并识别的过程包含了大量的冗余信息,降低了识别效率.在本文中,通过深度学习与压缩感知技术的结合,提出了一种基于联合感知矩阵的压缩学习目标识别技术(Target recognition technology based on a new joint sensing matrix for compressed learning,TRNPCL),使得探测器可快速生成目标图像多维压缩数据,且压缩数据可直接作为深度学习目标识别框架的输入数据,而无需再进行解压缩步骤.该方法不仅大大减小了深度学习框架的数据输入量,在与同等压缩比下的单空间域数据压缩学习方式相比较,还保持了较高的识别准确率.在未来,该方法有望成为一种更有效、更灵活的目标识别方法,并特别适用于指纹识别、人脸识别等应用领域.

Target recognition is gradually becoming an important technology to provide accurate target category in⁃formation in the field of automation,and most current target recognition methods are based on machine learning frame⁃works.Generally,the input data of the machine learning framework is raw image data,but in practical applications,the way that the detector obtains the raw image data and uses it as the input of the deep learning framework to achieve target recogni⁃tion is low efficient,contains a lot of redundant information,which reduces the recognition efficiency.In this paper,by com⁃bining machine learning and compressive sensing technology,a new target recognition method named as target recognition technology based on a new joint sensing matrix for compressed learning(TRNPCL)is proposed.Through the proposed method,the detector can quickly generate multi-dimensional compressed data of the target image,and the compressed data can be directly used as the input data of the deep learning target recognition framework without further decompression steps,which not only greatly reduces the amount of data input to the machine learning framework,but also maintains a high⁃er recognition accuracy compared with the single-space domain data compression learning method under the same compres⁃sion ratio.In the future,this method is expected to become a more effective and flexible target recognition method,and es⁃pecially suitable for fingerprint recognition,face recognition and other application fields.