基于卷积神经网络的标识牌识别技术

目前而言,我国标识识别技术正处于飞速发展阶段,具体体现在处理精度、再现性、灵活性、适用面、信息压缩等方面,但是,在实际发展过程中,该技术的发展还是受到了实际需求的限制.深度学习模型运算量大,难以在轻量级嵌入式设备上运行,工业生产中噪声种类繁多复杂,影响识别准确性.针对上述问题,本文提出一种基于卷积神经网络的标识识别技术,利用改进的Canny边缘检测算法,来增强对边缘信息提取时的鲁棒性,实现在高噪声环境下对标识牌精准提取.另外为了进一步提高识别准确率,本文利用CNN和椭圆拟合相结合的思路,把模型识别结果和椭圆拟合结果相结合来判别识别的准确性,在增加少量运算量的同时提高识别准确率.

Logo Recognition Technology Based on Convolutional Neural Network

At present, the logo recognition technology in China is being rapidly developed, which is embodied in processing accuracy, reproducibility, flexibility, applicability, and information compression. However, the development of this technology is still limited by actual demands. The deep learning model has heavy computation and is difficult to run on lightweight embedded devices. There are many and complex noises in industrial production, which affect the recognition accuracy. To solve the above problems, this study proposes a logo recognition technology based on the convolutional neural network. An improved Canny edge detection algorithm is used to enhance the robustness in edge information extraction, and signs are accurately extracted in a high-noise environment. In addition, to further improve the recognition accuracy, in the combination of Convolutional Neural Network (CNN) and ellipse fitting, this study combines the model recognition and ellipse fitting results to determine the recognition accuracy. This method improves the recognition accuracy while increasing a small amount of calculation.