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