基于MobileNetV2的圆形指针式仪表识别系统

针对目前指针式仪表识别任务在使用深度学习算法时存在模型参数量大、计算量大、准确率较低的问题，提出一种基于改进预训练MobileNetV2网络模型与圆形Hough变换相结合的圆形指针式仪表智能检测和识别系统。首先，采用Hough变换解决复杂场景内非圆形区域的干扰问题；然后，提取圆形区域以构建数据集；最后，使用基于改进预训练MobileNetV2网络模型对圆形指针式仪表进行识别。为客观反映所提模型的性能优劣，采用平均混淆矩阵来衡量模型性能。实验结果表明，该系统在圆形指针式仪表识别任务中的识别率达到99.76%。同时，将所提模型与其他5种不同的网络模型进行对比的结果表明，该模型与ResNet50的准确率最高，但在模型参数量和模型计算量方面，所提网络模型相较于ResNet50分别降低了90.51%和92.40%，可见该模型有助于进一步在移动端或嵌入式设备中部署和实现工业级的实时圆形指针式仪表检测和识别。

Circular pointer instrument recognition system based on MobileNetV2

Aiming at the problems of large number of model parameters, large computational cost and low accuracy when using deep learning algorithms for pointer instrument recognition task, an intelligent detection and recognition system of circular pointer instrument based on the combination of improved pre-trained MobileNetV2 network model and circular Hough transform was proposed. Firstly, the Hough transform was used to solve the interference problem of non-circular areas in complex scene. Then, the circular areas were extracted to construct datasets. Finally, the circular pointer instrument recognition was realized by using the improved pre-trained MobileNetV2 network model. The average confusion matrix was used to measure the performance of the proposed model. Experimental results show that, the recognition rate of the proposed system in the recognition task of circular pointer instruments reaches 99.76%. At the same time, the results of comparing the proposed model with other five different network models show that the proposed model and ResNet50 both have the highest accuracy, but compared with ResNet50, the proposed network model has the model parameter number and model computational cost reduced by 90.51% and 92.40% respectively, verifying that the proposed model is helpful for the further deployment and implementation of industrial grade real-time circular pointer instrument detection and recognition in mobile terminals or embedded devices.