Lu Xiaohu, Zhu Shisong, Lu Bibo
School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, China.
Sensors (Basel). 2024 Aug 15;24(16):5288. doi: 10.3390/s24165288.
In order to improve the reading efficiency of pointer meter, this paper proposes a reading method based on LinkNet. Firstly, the meter dial area is detected using YOLOv8. Subsequently, the detected images are fed into the improved LinkNet segmentation network. In this network, we replace traditional convolution with partial convolution, which reduces the number of model parameters while ensuring accuracy is not affected. Remove one pair of encoding and decoding modules to further compress the model size. In the feature fusion part of the model, the CBAM (Convolutional Block Attention Module) attention module is added and the direct summing operation is replaced by the AFF (Attention Feature Fusion) module, which enhances the feature extraction capability of the model for the segmented target. In the subsequent rotation correction section, this paper effectively addresses the issue of inaccurate prediction by CNN networks for axisymmetric images within the 0-360° range, by dividing the rotation angle prediction into classification and regression steps. It ensures that the final reading part receives the correct angle of image input, thereby improving the accuracy of the overall reading algorithm. The final experimental results indicate that our proposed reading method has a mean absolute error of 0.20 and a frame rate of 15.
为了提高指针式仪表的读数效率，本文提出了一种基于LinkNet的读数方法。首先，使用YOLOv8检测仪表表盘区域。随后，将检测到的图像输入到改进的LinkNet分割网络中。在该网络中，我们用部分卷积取代传统卷积，在不影响准确性的同时减少了模型参数数量。移除一对编码和解码模块以进一步压缩模型大小。在模型的特征融合部分，添加了CBAM（卷积块注意力模块）注意力模块，并用AFF（注意力特征融合）模块取代直接求和操作，增强了模型对分割目标的特征提取能力。在后续的旋转校正部分，本文通过将旋转角度预测分为分类和回归步骤，有效解决了CNN网络对0 - 360°范围内轴对称图像预测不准确的问题。它确保最终读数部分接收到正确角度的图像输入，从而提高了整体读数算法的准确性。最终实验结果表明，我们提出的读数方法平均绝对误差为0.20，帧率为15。
