Virtual point cloud three-dimensional target detection method based on supervised monocular depth estimation

Abstract

The invention relates to a virtual point cloud three-dimensional target detection method based on supervised monocular depth estimation, and the method comprises the steps: 1, carrying out the measurement through employing a laser radar, and collecting the depth information of a scene; and 2, training a monocular depth estimation model by using the data set obtained in the step 1. The beneficial effects of the invention are that: the camera is directly used as a main sensing means, the application of expensive sensors such as a laser radar in a three-dimensional target detection system is avoided, and meanwhile, the problems of joint calibration and data synchronization in a multi-sensor sensing method are directly avoided, the sensor deployment cost is further reduced, the dependence on alaser radar is reduced, the cost of a three-dimensional target detection method is reduced, and the application of the technology in various fields is promoted. Besides, the algorithm model is deployed to the edge equipment through offline training and online prediction modes so that the intelligent level of the edge equipment can be enhanced while the equipment calculation pressure can be relieved.

Description

technical field [0001] The invention belongs to the technical field of target detection, in particular to a virtual point cloud three-dimensional target detection method based on supervised monocular depth estimation. Background technique [0002] Target detection technology is one of the most important tasks in environmental perception, which mainly perceives the position and category of target objects through images. This technology is widely used in many fields such as industry, transportation, aerospace, medicine and so on. The traditional target detection technology is mainly based on two-dimensional detection, and the detection task of the target object is generated by generating a two-dimensional detection frame. In order to further improve the perception level, many research works in recent years have extended the two-dimensional detection frame to the three-dimensional detection frame to obtain a more detailed pose state of the target object. However, since the im...

AI Technical Summary

Problems solved by technology

This method directly uses lidar as the hardware device for environment perception, which is expensive and cannot be applied to general scenarios on a large scale.

The acquisition scheme of lidar and camera requires joint calibration between devices. If there are problems such as position deviation, it needs to be re-calibrated, and the process is relatively complicated.

In addition, this solution also has data synchronization problems. The frequency of the collected images is inconsistent with that of the point cloud data. It needs to be synchronized before the target detection can be performed.

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