融合相机与激光雷达的目标检测与尺寸测量

针对三维场景下的目标检测与尺寸测量任务,设计了一种融合激光雷达和相机传感器的三维目标检测和尺寸测量算 法。 使用基于卷积神经网络的二维目标检测器提取目标的二维检测框,结合图像中的二维检测框和几何投影关系获取包含物 体的三维视锥点云,由欧氏聚类方法获得物体的聚类点云,实现了物体的三维目标检测。 提出了基于目标二维检测框的改进尺 寸测量方案以替代原有点云聚类后得到的三维框信息,提高了物体尺寸测量的精度。 在现有数据集上评估测试了目标检测与 尺寸测量的精度,实验结果表明,二维目标检测器 YOLOv7 在检测数据集上的平均检测精度达到了 81%,改进尺寸测量方案在 物体尺寸测量时的测量误差在 5%以内,对于较远物体或较小物体的目标检测和尺寸测量也具有很好的效果。

Fusing camera and Lidar for object detection and dimensional measurement

A 3D object detection and size measurement algorithm is designed for object detection and size measurement tasks in 3D scenes, which fuses LIDAR and camera sensors. A 2D object detector based on convolutional neural networks is used to extract the 2D detection box of the object. The 3D point cloud containing the object is obtained by combining the 2D detection box in the image and the geometric projection relationship. The object clustering point cloud is obtained by the Euclidean clustering method, realizing 3D object detection. An improved size measurement scheme based on the 2D detection box of the object is proposed to replace the original 3D box information obtained after point cloud clustering, improving the accuracy of object size measurement. The accuracy of object detection and size measurement is evaluated and tested on existing datasets. Experimental results show that the average detection accuracy of the 2D object detector YOLO v7 reaches 81% on the detection dataset, and the measurement error of the improved size measurement scheme is within 5% for object size measurement. It also has good performance in object detection and size measurement for objects that are farther away or smaller. Keywords:object detecti