Three-dimensional (3D) object detection is an important task in the field of machine vision, in which the detection of 3D objects using monocular vision is even more challenging. We observe that most of the existing monocular methods focus on the design of the feature extraction framework or embedded geometric constraints, but ignore the possible errors in the intermediate process of the detection pipeline. These errors may be further amplified in the subsequent processes. After exploring the existing detection framework of keypoints, we find that the accuracy of keypoints prediction will seriously affect the solution of 3D object position. Therefore, we propose a novel keypoints uncertainty prediction network (KUP-Net) for monocular 3D object detection. In this work, we design an uncertainty prediction module to characterize the uncertainty that exists in keypoint prediction. Then, the uncertainty is used for joint optimization with object position. In addition, we adopt position-encoding to assist the uncertainty prediction, and use a timing coefficient to optimize the learning process. The experiments on our detector are conducted on the $KITTI$ benchmark. For the two levels of easy and moderate, we achieve accuracy of 17.26 and 11.78 in $A{P}_{3D}$, and achieve accuracy of 23.59 and 16.63 in $A{P}_{BEV}$, which are higher than the latest method KM3D.

All walks of life actively promoted emerging technologies when modern agricultural science and technology from the West were introduced to China in the late Qing Dynasty. Despite agricultural machinery being highly popular during the more than 100 years since, traditional farm tools have not been completely replaced and have instead gradually become symbols representing traditional farming culture. From the perspective of ecology and health, this paper discusses the values, scientific notions and corresponding styles of production and life associated with traditional agricultural tools as well as the multicultural values regarding traditional agricultural culture in modern society in China.