为实现易拉罐灌装过程中喷码字符实时检测, 提出了一种基于卷积神经网络的实时检测方法。该方法首先对采集的图像进行直方图均衡化和 OSTU处理, 然后对图像进行形态学膨胀操作, 通过连通域面积法提取出喷码字符区域并进行旋转矫正, 再采用投影法将字符区域分割为单个字符, 在离线状态下采用卷积神经网络对字符进行训练, 从而在在线检测时进行识别。实验表明, 该方法检测一帧图像平均时间为 46 ms, 准确率达 98.97%, 实时性和准确性较高, 可以满足工业易拉罐喷码字符在线实时检测要求。

In order to achieve the real-time detection of Coding characters in the process of filling cans, a real-time detection method based on convolutional neural network is proposed. This method initially adopts the histogram equalization and OSTU to deal with the images and then operates the images by the morphological inflation method. Besides, the region of the printed code characters is extracted by the area method of connected domain and then rotates and corrects this region. By using the projection method, the region is divided into single characters which will be trained by the convolutional neural network under the offline state. All above procedures are done in order to recognize the characters while doing the online detection. Experiments show that the average time of every detected image is 46 ms and its accuracy achieves 98.97% which show high instantaneity and accuracy. Thus, it can meet the demand of the real-time detection of industrial cans characters.