基于改进的YOLOv4彩色数字仪表读数识别方法

Improved YOLOv4 color digital instrument reading recognition method

针对传统数字仪表识别方法计算量大、实时性不够和精度较低的问题,研究了一种深度学习与图像处理相结合的识别方法。为减少计算量,在YOLOv4网络中引入GhostNet作为基础网络,同时在YOLOv4非主干网络中引入深度可分离卷积以及Ghost模块来减少参数,并使用h-swish激活函数提高精度。为了凸显二值化过程中彩色仪表的色彩信息,在数字提取过程,研究了一种基于彩色模型多阈值分割的数字二值化方法,对RGB图像的色彩主色进行增强,然后转化为HSI图像,并且通过多阈值处理将满足条件的像素点保留,从而得到二值化图像,相比于传统图像预处理算法可以更好地提取数字信息。实验结果表明改进的识别方法在测试集上准确率达到87.98%mAP,检测速度提高到37.2 FPS,在数字仪表定位识别中效果显著。

The traditional digital instrument recognition method has a large amount of computational amount, not enough real-time, low accuracy. This paper studies a meter identification method is studied in combination with deep learning and image processing. In order to reduce the amount of computation, YOLOv4 target detection network is used and GhostNet is adopted as YOLOv4 basic network. At the same time, the depthwise separable convolution and Ghost module can be introduced in YOLOv4 to reduce the amount of parameters, and the h-swish activation function is applied to increase the accuracy. In order to highlight color information in the image binarization process, a digital binarized method is studied based on color model multi-threshold segmentation. The main color of RGB image is enhanced, and then converts to an HSI image, and then the pixel point satisfying the condition will be reserved by multi-threshold processing, thereby obtaining a binarized image. Digital information can be better extracted in comparison with traditional image pretreatment algorithms. Experimental results show that the proposed method of reaches 87.98%mAP on the test data set, and detection speed is increased to 37.2 FPS, and then the effect is significant in digital instrument positioning and digital inspection.