|Place of Conferral||北京|
|Keyword||小目标检测 小目标识别 实时性 Fpga Dsp|
1、针对红外图像中车辆小目标的特点，提出了一种基于边缘概率分布与亮度信息相融合的检测方法，并在课题组设计的基于 FPGA 和 DSP 的嵌入式智能图像处理卡上进行了实现。该方法将改进的Sobel算子提取的边缘特征用于朴素贝叶斯分类器进行训练，同时又融合了车辆的亮度信息，用于在上位机上构建训练模型；然后，将构建的模型在嵌入式智能图像卡上进行硬件实现，使其能够满足实时检测系统的需要。
|Other Abstract|| As a very active research area in the field of computer vision and artificial intelligence, object detection and recognition has been widely used in industrial, military, medical, security monitoring and many other fields. However, with the increasing demands of practical application, the requirement for real-time performance in object detection and recognition system is becoming higher. In recent decades, although researches on object detection and recognition algorithm have made satisfactory progress, researches reconciling the demands for the real-time performance and accuracy at the same time on object detection and recognition are very limited. There are still many problems to be solved in order to build a real-time, robust and practical object detection and recognition system. This dissertation deeply studies the problem on small object detection and recognition using embedded system based on DSP and FPGA, aiming at improving the precision and real-time performance, and solves several key technical problems, which lays a solid foundation for developing real-time object detection and recognition system with practical value.|
The research work of this dissertation can be summarized as the following aspects:
1. According to the characteristics of vehicles captured by an infrared camera, an infrared small object detection algorithm based on Bayesian edge probability distribution and brightness features is proposed and implemented on an embedded image processing card based on FPGA and DSP, which was designed by our research group. The algorithm puts edge features of the training samples extracted by an improved Sobel operator into the Naive Bayes Model, and then fuses with brightness features to construct the detector template on the host computer. Then, the detector template is hardware implemented on the image acquisition and processing board to meet the requirements of the real-time vehicle detection system.
Firstly, image filtering, edge extraction, image binaryzation, Naive Bayes based object detection and fusing with brightness features are hardware implemented on FPGA, objects' coordinates are also calculated on FPGA and then passed to the SRAM. Secondly, DSP gets the coordinates from the SRAM, marks vehicle objects with rectangles according to the coordinates, summarizes the total number of detected vehicle objects and then transmits the detection results to PC. Finally, PC displays the detection results and modifies detection parameters of FPGA online, the modified parameters are passed from PC to FPGA through DSP by the 100-Mbps Ethernet interface on the image processing board. Experiment results show that the method can not only detect multiple vehicle objects in real-time with a good computation complexity and accuracy, but also can detect vehicles which are partially occluded and detect vehicles whose left lateral sides have an oblique angle with the camera, which proves that the system has a good robustness and generalization performance.
2. In order to recognize cars, planes, vehicles, soldiers and other small objects in the battlefield environment, a real-time object recognition method of wavelet moment-based back-propagation(BP) neural network classifier is proposed and hardware implemented on a low-power embedded image acquiring and processing board. The method gives the discrete form of wavelet moment extraction algorithm, improves the wavelet moment extraction algorithm and BP neural network classifier to make a fixed point realization of them on the DSP, and reduces the time complexity.
To improve the processing speed of the algorithm, image filtering, adaptive threshold calculation and image binaryzation are hardware implemented on FPGA, while the improved wavelet moment extraction algorithm and BP neural network classifier are realized in a fixed point form on the DSP. Experiment results show that the method can recognize single object and multiple objects of similar shape correctly and efficiently. The proposed method is of good computation complexity and has scaling, translation and rotation invariance, and can recognize small objects in real-time correctly even though they have translation, rotation and scaling change.
|First Author Affilication||Institute of Automation, Chinese Academy of Sciences|
|谷姣姣. 基于嵌入式视觉系统的小目标实时检测与识别技术研究[D]. 北京. 中国科学院大学,2017.|
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