为提高车辆目标检测精度,针对传统视觉传感器光照敏感性、空间感知性差等缺点,采用激光雷达传感器,提出一种基于ResNet-MLP二阶段模型的车辆目标检测算法.该算法对点云鸟瞰图的映射方式进行改进,使其保留点云高度特征,并通过改进后的ResNet进行点云特征的提取,最后使用并行多层感知机网络对车辆目标分类和位置回归.采用KITTI的 3D Object数据集进行验证,通过与PointNet++和VoxelNet方法进行对比实验,结果发现,交并比(IOU)较高时 3 种方法的检测精度均有所下降,但相对于其他 2 种算法,本算法检测精度更高,运行速度更快,可为未来自动驾驶车辆的实时感知方面提供技术支撑.
Vehicle target detection algorithm based on ResNet-MLP modeling
Aiming at the disadvantages of low sensitivity and poor spatial perception of tradi-tional visual sensors,a vehicle target detection algorithm based on ResNet-MLP two-stage model is proposed to enhance the accuracy of vehicle target detection by using LiDAR sen-sor.The algorithm improves the mapping of point cloud bird's-eye view so that it retains the point cloud height features,and the point cloud features are extracted by the improved Res-Net.Finally,a parallel multilayer perceptron network is used to classify vehicle targets and regress the position.KITTI's 3D Object dataset is used for validation,and through compar-ison experiments with PointNet++and VoxelNet methods,it is found that the detection accuracy of the three methods decreases when the IOU is higher,but compared to the other two algorithms,the proposed algorithm has better detection accuracy and higher running speed,which can provide technical support for the real-time perception of self-driving vehi-cles in the future.
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