CAJ | 학술논문

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。
위실현역랍관관장과정중분마자부실시검측，제출료일충기우권적신경망락적실시검측방법。해방법수선대채집적도상진행직방도균형화화OSTU처리，연후대도상진행형태학팽창조작，통과련통역면적법제취출분마자부구역병진행선전교정，재채용투영법장자부구역분할위단개자부，재리선상태하채용권적신경망락대자부진행훈련，종이재재선검측시진행식별。실험표명，해방법검측일정도상평균시간위46 ms，준학솔체98.97%，실시성화준학성교고，가이만족공업역랍관분마자부재선실시검측요구。
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.