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基于深度学习的电表字符缺陷检测方法

Characters Defect Detection Method of Meter Based on Deep Learning

  • 摘要: 针对传统电表显示屏字符缺陷检测准确率和效率较低等问题,设计一种基于深度学习的电表字符缺陷分步检测方法。对相机采集的图片进行特征标注,利用电表字符区域分块算法对智能电表一次检测,实现对电表字符区域的精准分类;对裁剪后的每块区域字符进行缺陷特征标注,再利用电表字符缺陷检测算法分别对九块区域进行二次检测,解决电表字符缺陷检出率低的问题;对所提方法进行实验验证。结果表明,电表区域分块检测精度达99.9%、速度达0.6 s/张,字符缺陷检测精度达98%、速度达1.04 s/块,单个电表的检测时长为1.64 s,所提方法可满足电表实际生产中检测精度和时间的需求。

     

    Abstract: Aiming at the problems of low accuracy and low efficiency of characters defect detection in traditional meter display screen, a step-by-step detection method based on deep learning was designed. Firstly, the images collected by the camera were marked with features,the block algorithm of characters area in meter was used to detect meters, which realized the accurate classification of the characters area of the meter. Then, the defect features were marked for each region characters after cutting, and the algorithm of characters defect detection was used to detect the nine areas, which solved the problem of low detection rate of meter characters defects. Finally, the proposed method was verified by experiments. The results show that the block accuracy can reach 99.9%, the speed can reach 0.6 s/picture, the characters defect detection accuracy can reach 98%, the speed can reach 1.04 s/block, and the detection time of a meter is 1.64 s. The proposed method can meet the requirements of detection accuracy and time in the actual production of meters.

     

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