基于细节注意力卷积神经网络的仪表自动化识别方法

Detail-attention convolutional neural network for meter recognition

传统仪表自动化识别方法受光照、噪声等干扰因素限制,难以广泛应用.近年来基于卷积神经网络的对圆心、最大最小量程等关键点检测的方法难以处理仪表的多样性.本文对指针式仪表的自动化识别进行研究,根据人工读数方式提出了先对刻度与指针进行定位,再通过指针与刻度的相对位置进行读数的方法,对各类仪表兼容性好.为了能够稳定、高精度地提取刻度线,本文根据仪表刻度线区域低占比特征设计了meter scan net (MSN)模型,通过MSN预测刻度线区域的热图. MSN通过本文设计的细节注意力detail-attention (DA)模块,可以保留细节特征,关注仪表区域响应,有助于最后的精细分割.后处理利用预测的热图进行圆拟合获取刻度弧线,在刻度弧线的法向方向取一定长度进行采样,根据采样数据定位刻度与指针尖位置.同时针对刻度丢失的情况,本文基于频域分析的方法复原丢失刻度,提高了方法的适用性.最后在测试集数据上大量的实验证明本文提出的方法具有精度高、仪表兼容性好等特点.

The classical meter recognition method is severely affected by light and noise interference in the recognition of meters, which limits its generalization ability. Recent methods based on convolutional neural networks cannot handle the diversity of meters via detection of the key points(center of circle, maximum and minimum range). In this study, we investigate the recognition of a mechanical meter.According to the manual reading paradigm, the scale and pointer are first positioned, and the reading value is then determined based on the relative position of the pointer and scale, which is compatible with different meters. To extract the tick marks of meters in a stable and precise scheme, Meter scan net(MSN) is designed based on the low-occupancy features of the meter’s tick area to predict the heatmap. Utilizing our proposed Detail-Attention module, MSN can greatly retain detailed features and focus on the tick area,which is helpful for the final segmentation. Post-processing includes using the output of MSN to perform circle fitting to obtain the scale arc, sampling along the normal phase direction of the scale arc, and locating the scale and pointer position. To handle cases of missing tick marks, a frequency-based analysis method is proposed to restore the missing tick marks, which enlarges the application scope of our method. Finally, extensive experiments are conducted that reveal that the proposed method has high accuracy and is compatible with different types of meters.