Mr. Zhuo He Profile

Zhuo He

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Proceedings Article | 31 January 2020 Paper

A weak and small target detection algorithm based on TMS320C6678 for convolutional neural network

Zhuo He, Jin Lu, Chen Xu, Tianzhang Nan

Proceedings Volume 11427, 114273D (2020) https://doi.org/10.1117/12.2553010

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TMS320C6678 is a multi-core DSP processor with higher processing speed compared with other single-core DSPs. This paper proposes a convolutional neural system based on the technical requirements of TMS320C6678 processing target detection technology to enhance real-time and reduce false alarm rate. Infrared weak target detection algorithm for networks. Pre-processing methods such as image denoising and enhancement and Kronecker product up sampling are used to expand the target size and enhance the morphological features while reducing the difficulty of detecting weak targets. Then the design is more suitable for detecting the convolutional neural network structure of weak targets. Extracting the deep features of infrared weak targets can accurately detect weak targets in infrared images while effectively eliminating various disturbances such as features and clouds. The simulation results show that the target detection probability of the algorithm is 95%, with low time complexity, strong resistance to ground and cloud interference, and high detection accuracy. At present, the core part of the algorithm is transplanted into TMS320C6678, the processing speed is much higher than the simulation result, the real-time performance is confirmed, and it has high engineering application value.

Proceedings Article | 12 December 2018 Paper

Research on the application of infrared weak and small target detection algorithm based on SVM posterior probability in weekly vision search system

Zhuo He, Jinsuo Zhang, Xu Chen, Tianzhang Nan

Proceedings Volume 10846, 108462S (2018) https://doi.org/10.1117/12.2505583

KEYWORDS: Target detection, Detection and tracking algorithms, Infrared radiation, Infrared detectors, Infrared imaging, Image filtering, Image classification, Machine learning, Algorithm development, Infrared cameras

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Infrared weak and small target detection has important application value in military field, and is a hot research topic in the field of target detection. With the research and development of technology, some guiding and innovative detection algorithms are emerging, especially the advantage of machine learning algorithm. In this paper, the infrared week and small target detection is considered as the two classification problem of target and background, and an infrared week and small target detection algorithm based on multiple features SVM posterior probability is proposed and applied to the weekly vision search system. In the experiment, the SVM classification model is obtained by using 8 good segmentation characteristics as the main reference value of the identification target and background, and training the training set through a large number of target and background samples. Finally, the SVM posterior probability is selected as the output of the detection result.The simulation experiment of this algorithm and the application experiment in the transplant week search system show that the method of this method has higher target detection probability (not less than 95%),the false alarm probability is lower, and the time complexity of the algorithm is low, and the hardware resource is less, and it has a good prospect of engineering application.

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