基于障碍物和车位检测的单阶段多任务YOLO-Parking算法研究

Research on single-stage multi-task YOLO-Parking algorithm based on obstacle and parking space detection

文章提出一种基于YOLOv4的端到端多任务网络模型用于自动泊车系统中的感知任务,以环视图像(around view monitor, AVM)作为网络输入,基于卷积网络提取图像特征信息,通过YOLO和DMPR-PS(directional marking-point regression-parking slot)检测头实现停车位与障碍物并行检测。在PS 2.0公开数据集上进行验证的结果表明,所提出的多任务检测方法能够同时检测停车位和障碍物,障碍物识别平均精度均值达到89.72%,车位识别查准率达到93.53%,网络检测速率为34.0帧/s,在满足自动泊车感知任务需求的同时提升了系统的检测效率。该文研究成果对自动泊车感知技术的发展具有一定的意义。

This paper proposes an end-to-end multi-task network model based on YOLOv4 for perception tasks in automatic parking systems.The model takes around view monitor(AVM)images as network inputs and extracts image feature information using convolutional networks.By utilizing YOLO and directional marking-point regression-parking slot(DMPR-PS)detection heads,it achieves parallel detection of parking spaces and obstacles.Validation on the PS 2.0 public dataset demonstrates that the proposed multi-task detection method can simultaneously detect parking spaces and obstacles,with an average precision of 89.72%for obstacle recognition and a precision rate of 93.53%for parking space recognition.The network achieves a detection rate of 34.0 frames per second,meeting the requirements of automatic parking perception tasks while improving detection efficiency.This research holds implications for the development of automatic parking perception technology.