Sprayed character online visual detection method based on convolutional neural network

The invention provides a sprayed character online visual detection method based on convolutional neural network. The sprayed character online visual detection method comprises the steps of dividing characters in an image under an offline status, classifying the characters, constructing a character repertoire, and performing training through an improved convolutional neural network learning method to form a stable classifier; shooting pictures, dividing the characters and classifying the characters in real time during online detection, and removing unqualified products. By means of the sprayed character online visual detection method, real-time performance is ensured while detection accuracy is improved greatly, and requirements for high real-time performance and accuracy of online detection process of the sprayed characters at bottoms of pop-top cans can be met.