Abstract:

Aiming at the problem of vehicle object detection in highway scenes, an improved YOLOv4 network is proposed to detect vehicles in traffic scenes. Firstly, a multi-weather, multi-period, multi-scene vehicle dataset is proposed, and vehicle detection models are obtained based on the datasets. Secondly, a multi-label detection method is proposed, and a constraint relationship between multiple labels is established to obtain more complete vehicle information. Finally, an image stitching detection method is proposed, which connects multiple images through the stitching layer for vehicle detection, so as to improve the running speed of the network. Experimental results show that the diversified dataset improves the accuracy of vehicle detection, reduces the false detection and missed detection of vehicle objects, and the improved network structure greatly improves the detection speed. The above methods can provide references for vehicle detection and practical applications in highway scenes.

Key words: object detection, deep learning, object dataset, image process, multi-label

摘要：

针对高速公路场景下的车辆目标检测问题，提出了一种改进的YOLOv4网络对交通场景下车辆目标进行检测的方法；制作了一个多天候、多时段、多场景的车辆目标数据集，并依据数据集得到检测模型；提出多标签检测方法，并在多标签之间建立约束关系，得到更完善的车辆信息；提出了一个图像拼接检测方法，将多幅图像通过拼接层连接后进行车辆检测，以此提升网络的运行效率。实验结果表明，多样化数据集提高了车辆检测精度，减少了车辆目标的误检、漏检，同时改进的网络结构较大提升了检测速度，上述方法可以为高速公路场景下的车辆目标检测与实际应用提供参考。

关键词: 目标检测, 深度学习, 目标数据集, 图像处理, 多标签