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Monocular multi-object detection and localization in 3D space has been provento be a challenging task. The MoNet3D algorithm is a novel and effectiveframework that can predict the 3D position of each object in a monocular imageand draw a 3D bounding box for each object. The MoNet3D method incorporatesprior knowledge of the spatial geometric correlation of neighbouring objectsinto the deep neural network training process to improve the accuracy of 3Dobject localization. Experiments on the KITTI dataset show that the accuracyfor predicting the depth and horizontal coordinates of objects in 3D space canreach 96.25\% and 94.74\%, respectively. Moreover, the method can realize thereal-time image processing at 27.85 FPS, showing promising potential forembedded advanced driving-assistance system applications. Our code is publiclyavailable at https://github.com/CQUlearningsystemgroup/YicongPeng.

MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time