改进Faster R-CNN的汽车仪表指针实时检测

针对产业化的汽车仪表指针人工视觉检测效果差、检测速度慢和实时性低等问题，本文提出了一种改进的Faster R-CNN汽车仪表指针实时检测算法。通过改进原始的RoI网络层结构，实现小目标高低层特征之间的完整传递；采用双线性内插算法替代两次量化操作，使得特征聚集变成连续的过程，能够有效减少计算时间；最后将工业机采集的视频数据，预处理成VOC格式数据集进行训练，调整超参数得到改进汽车仪表指针检测模型。实验结果表明:所提出的方法能够快速、准确地实现汽车仪表指针检测，单张图片的平均检测时间为0.197 s，平均检测精度可达92.7%。在不同类别仪表指针的迁移实验中，展示了良好的泛化性能。

Improved Faster R-CNN vehicle instrument pointer real-time detection algorithm

This paper proposes an improved Faster R-CNN vehicle instrument pointer real-time detection algorithm to solve the problems of the poor artificial visual detection effect, slow detection speed, and low real-time performance of industrialized vehicle instrument pointers. First, complete transfer between the high- and low-layer features of a small target is realized by improving an original RoI network layer structure. Subsequently, continuous feature aggregation reduces calculation time using a bilinear interpolation algorithm to replace two quantization operations. Finally, video data collected by an industrial machine are preprocessed into a VOC format data set for training, and hyperparameters are adjusted to obtain an improved vehicle instrument pointer detection model. Experimental results show that the proposed method can quickly and accurately detect the vehicle instrument pointer. The average detection time of a single picture is 0.197 s, and the average detection accuracy can reach 92.7%. The good generalization performance of this method is demonstrated in the migration experiment of different instrument pointer types.