基于不规则目标检测的指针式仪表读数识别方法

Reading Recognition Method for Pointe Meters Based on Irregula Object Detection

针对目前指针式仪表读数识别方法流程多、累计误差大、对倾斜图像识别效果差的问题,提出一种基于不规则目标检测网络的指针式仪表读数识别方法。首先构建校准网络结构,提取不规则目标顶点坐标,实现对图像自动进行透视变换,强化整体网络对倾斜样本的学习性能;随后利用卷积神经网络直接提取图像特征,实现读数信息的回归任务,减少方法步骤;最后整合模型,使倾斜校准与读数识别任务通过同一个可反向传播的神经网络学习并实现。实验表明,该方法提高了对倾斜仪表图像的读数识别精度,读数流程短、识别效率高。

Aiming at the problems of multiple processes,large cumulative errors,and poor recognition performance for tilted imagesin current pointer instrument reading recognition methods,a pointer instrument reading recognition method based on irregular object detection network was proposed.Firstly,a calibration network structure was constructed,irregular target vertex coordinates was extracted,and perspective transformation was automatically performed on the image to enhance the learning performance of the overall network for tilted samples.Subsequently,convolutional neural networks were used to directly extract image features,a-chieving the regression task of reading information and reducing method steps.Finally,the model was aggregated to enable tilt cal-ibration and reading recognition tasks to be learned and implemented together through a backpropagation neural network.The ex-periment shows that the method improves the reading recognition accuracy of inclined instrument images,with a short process and high recognition efficiency.