Vehicle Point Cloud Target Detection With Sparsity-Adaptive Convolution
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Solution Overview
Problem
Conventional target detection methods are inadequate for accurately and timely detecting remote targets, especially at high speeds, leading to insufficient safety distances and increased driving risks.
Innovation Solution
A target detection method that utilizes sparse convolutions and attention mechanisms to determine convolution dilation rates based on point cloud grid sparsity, extracting and fusing global features from both remote and close targets, enhancing detection accuracy and reducing calculation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional target detection methods are used, then close targets can be detected, but remote targets cannot be detected accurately and timely
Solution Approach 1:
The patent divides the point cloud space into multiple three-dimensional point cloud grids, allowing different regions (remote and close targets) to be processed independently. This segmentation enables the system to handle remote targets without compromising the detection of close targets, resolving the contradiction between detection accuracy and time loss.
Solution Approach 2:
The patent introduces a hierarchical structure with super grids at different resolution levels. By transforming point cloud grids into super grids and processing them at multiple dimensions, the system can detect remote targets accurately while maintaining real-time performance, addressing both accuracy and time requirements.
2Measurement precision
If higher detection accuracy for remote targets is achieved, then safety distance can be ensured, but computational load increases
Solution Approach 1:
The patent extracts and processes only the essential features from point cloud grids by determining sparsity and selectively applying dilated convolutions. This extraction approach reduces computational load while maintaining detection accuracy for remote targets, resolving the contradiction between accuracy and energy consumption.
Solution Approach 2:
The patent dynamically adjusts convolution dilation rates based on the sparsity of point cloud grids. By changing the dilation rate parameter according to data distribution, the system optimizes computational resources, achieving high accuracy for remote targets without excessive computational load.
3Length of stationary object
If convolution dilation rate is increased to detect remote targets, then detection range improves, but calculation time increases
Solution Approach 1:
The patent makes the convolution dilation rate dynamic by adjusting it based on the sparsity of point cloud grids. This dynamic adjustment allows the system to extend detection range when needed while reducing calculation time when sparsity is low, resolving the contradiction between detection range and calculation time.
Data Source
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AI summary
The disclosure relates to the technical field of autonomous driving, and specifically provides a target detection method, a computer device, a computer-readable storage medium, and a vehicle, to solve the problem of detecting a target in a timely and accurate manner. For this purpose, the method of the disclosure includes: rasterizing point cloud space of three-dimensional point clouds in a vehicle driving environment to form a plurality of three-dimensional point cloud grids, and using point cloud grids including three-dimensional point clouds as target point cloud grids; determining a convolution dilation rate based on sparsity of the target point cloud grid; dilating a sparse convolution based on the convolution dilation rate; extracting a point cloud grid feature of the target point cloud grid by using a dilated sparse convolution; weighting the point cloud grid feature by using an attention mechanism to obtain a global point cloud feature; and performing target detection based on the global point cloud feature. In this way, both a remote target and a close target can be accurately detected. In addition, using the sparse convolution for detection can reduce the calculation amount and improve the detection efficiency.