改进YOLOv3算法的车辆信息检测

车辆信息检测是车型识别在智慧交通领域中的首要任务。针对现有的车辆信息检测技术在检测速度、精度以及稳定性方面存在的问题，提出了基于YOLOv3的深度学习目标检测算法——YOLOv3-fass。该算法以DarkNet-53网络结构为基础，删减了部分残差结构，降低了卷积层的通道数，添加了1条下采样支路和3个尺度跳连结构，增加了一个检测尺度，并通过K-均值聚类与手动调节相结合的方法计算出12组锚框值。最后通过迁移学习机制对YOLOv3-fass算法进行微调。在自研的车辆数据集上，YOLOv3-fass算法与YOLOv3、YOLOv3-tiny、YOLOv3-spp算法以及具有ResNet50和DenseNet201经典网络结构的算法做了对比实验，结果表明YOLOv3-fass算法能够更精准、高效、稳定地检测到车辆信息。

Vehicle information detection based on improved YOLOv3 algorithm

Vehicle information detection is the primary task of vehicle type identification in the field of intelligent transportation. Based on deep learning YOLOv3 (You Only Look Once Version 3) model, a new YOLOv3-fass object detection algorithm was proposed to address some problems existing in vehicle information detection technology such as detection speed, accuracy and stability. In this improved algorithm, based on DarkNet-53 network structure, some residual structures were deleted, and a number of channels of convolutional layer were reduced; a down-sampling branch, three scale-hopping connection structures, and one detection scale were added; and twelve groups of anchor frame values were calculated through the means of K-means clustering algorithm combined with manual setting. Finally, YOLOv3-fass algorithm was fine-tuned through the migration learning mechanism of multi-stage pre-training. YOLOv3-fass algorithm was compared with YOLOv3, YOLOv3-tiny, YOLOv3-spp and two algorithms with ResNet50 and DenseNet201 on the vehicle data set. The experimental results show that YOLOv3-fass algorithm can detect vehicle information more accurately, efficiently and stably.