3D Object Detection via Monocular Camera Parameter Optimization
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Solution Overview
Problem
Current 3D object detection technologies relying on binocular cameras and laser radars are complex, costly, and have low detection efficiency and precision, making them unsuitable for widespread use in intelligent transportation and smart city applications.
Innovation Solution
A method utilizing a monocular camera to determine 2D and initial 3D image parameters, with candidate parameters optimized based on a disturbance range and 2D image parameters to improve the accuracy and efficiency of 3D object detection, reducing complexity and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binocular cameras and laser radars are used for 3D object detection, then detection precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a monocular camera to capture 2D images and reconstructs 3D information through computational algorithms, effectively creating a virtual copy of the 3D detection capability that would otherwise require complex hardware like binocular cameras or laser radars. This allows achieving 3D detection precision without the complexity of multiple sensors
Solution Approach 2:
The patent replaces the mechanical/optical system of binocular cameras and laser radars with a computational imaging approach using a single camera combined with algorithmic processing. The 3D detection is achieved through image processing and parameter optimization rather than through complex mechanical sensing systems
2Measurement precision
If binocular cameras and laser radars are used for 3D object detection, then detection precision is improved, but detection efficiency decreases
Solution Approach 1:
The patent performs preliminary 2D parameter detection using the monocular camera before conducting 3D reconstruction. By first identifying 2D image parameters and using them to guide the 3D parameter optimization, the system avoids exhaustive search and achieves faster detection efficiency while maintaining precision
3Measurement precision
If binocular cameras and laser radars are used for 3D object detection, then detection precision is improved, but cost increases
Solution Approach 1:
The patent replaces expensive, complex sensing hardware (binocular cameras, laser radars) with a single, inexpensive monocular camera. The cost of the detection system is dramatically reduced while maintaining 3D detection precision through computational methods rather than expensive hardware
4Device complexity
If monocular camera is used for 3D object detection, then device complexity and cost are reduced, but detection precision deteriorates
Solution Approach 1:
The patent optimizes 3D detection precision by systematically adjusting and optimizing parameters such as disturbance range, candidate parameter selection, and optimization objectives. Through parameter optimization, the system achieves high detection precision despite using a simple monocular camera
Solution Approach 2:
The patent implements feedback mechanisms where 2D image parameters are used to guide and refine the 3D parameter optimization process. The system continuously refines candidate 3D parameters based on feedback from 2D detection results, achieving high precision through iterative optimization
Data Source
AI summary
The present disclosure provides a three-dimensional (3D) object detection method, a 3D object detection apparatus, an electronic device, and a readable storage medium, belonging to a field of computer vision technologies. Two-dimensional (2D) image parameters and initial 3D image parameters are determined for a target object. Candidate 3D image parameters are determined for the target object based on a disturbance range of 3D parameters and the initial 3D image parameters determined for the target object. Target 3D image parameters are selected for the target object from the candidate 3D image parameters determined for the target object based on the 2D image parameters. A 3D detection result of the target object is determined based on the target 3D image parameters.