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Three-dimensional object detection has been substantially improved with the use of expensive LiDAR and stereo vision systems in intelligent driving. The less-expensive and more scalable solution of monocular 3D object detection, however, remains a key challenge. This study primarily explores real-time pseudo 3D object detection with monocular vision and designs a single-shot RPN model, VKP-P3D, which relies purely on visual feature extraction. Through a multiscale feature fusion and an attention mechanism module, this model obtains high-dimensional feature representations during the feature extraction phase. In the detection head of the VKP-P3D model, the pseudo 3D object detection is obtained by regressing 2D bounding box and the visible key points within the image coordinate of the 3D box from the camera&#x2019;s perspective. Finally, assuming flat ground and considering geometric parameters of the camera, the object&#x2019;s 3D information can be extracted. To verify the effectiveness of the proposed algorithm, we constructed two pseudo 3D object detection datasets based on visible key points and compared with current state-of-the-art real-time object detector. Results showed that the proposed model has high detection accuracy and speed.

VKP-P3D: Real-time Monocular Pseudo 3D Object Detection Based on Visible Key Points and Camera Geometry