Abstract: Monocular 3D object detection aims to complete 3D object detection using monocular images,and most existing monocular 3D object detection algorithms are based on classical 2D object detection algorithms.To address the issue of inaccurate instance depth estimation through direct regression in monocular 3D object detection algorithms,which leads to poor detection accuracy,a monocular 3D object detection algorithm based on height-depth constraint and edge feature fusion is proposed.In the instance depth estimation method,the height-depth constraint is calculated by the instance 3D height and 2D height under the geometric projection relationship,mainly converting the prediction of instance depth into the prediction of 2D height and 3D height of the object.To address the issue of object truncation at image edges in monocular images,an edge fusion module based on depth separable convolution is used to enhance the feature extraction of edge objects.For the multi-scale problem caused by the proximity and distance of objects in the image,a multi-scale mix attention module based on dilated convolution is designed to enhance the multi-scale feature extraction of the highest layer feature map.Experimental results demonstrate the effectiveness of the proposed method,as it achieves a 7.11% improvement in car category detection accuracy compared to the baseline model on the KITTI dataset,outperforming the current methods.

Key words: Monocular 3D object detection, Height-Depth constraint, Edge fusion, Multi-scale feature, Attention mechanism