基于ZYNQ和CNN模型的服装识别系统

商品检索是电商行业智能化发展的一个重要的问题.本设计实现了基于ZYNQ和CNN模型的服装识别系统.利用TensorFlow训练自定义网络，定点化处理权重参数.利用ZYNQ器件的ARM+FPGA软硬件协同的特点搭建系统，使用ARM端OpenCV进行图像预处理，FPGA端CNN IP进行实时识别.ARM与FPGA之间实现了权重可重加载结构，无需修改FPGA硬件而实现在线升级.系统采用fashion-minist数据集作为网络训练样本，根据系统资源配置CNN IP的加速引擎的数量来提高卷积运算的并行性.实验表明，本系统针对电商平台下的图片能够实时准确识别和显示，准确率达92.39%.在100 MHz工作频率下，图像处理速度每帧可达到1.361 ms，功耗仅为0.53 W.

Fashion Recognition System Based on ZYNQ and CNN Model

Commodity retrieval is fundamental to the intelligent development of the e-commerce industry. This study is concerned with a fashion recognition system which bases on ZYNQ and CNN model, trains the custom network with TensorFlow and processes the weighs by using fixed-point calculations. This system applies ARM + FPGA software and hardware coordination method of the ZYNQ device to construct its framework, ARM to preprocess image by OpenCV and the CNN IP of FPGA to synchronously recognize the image. A weight-reloadable structure is implemented between ARM and FPGA, so online upgrade could be realized without modifying the FPGA hardware. The system uses fashion-minist datasets as the sample in the network training and improves the parallelism of convolution operations by increasing the acceleration engines of CNN IP. According to the experiment, the system realizes accurate and real-time identification and display for actual pictures of the e-commerce platform and its accuracy reaches 92.39%. The image processing speed can reach 1.261 ms per frame and the power consumption is only 0.53 W at 100 MHz.