3D Target Detection Using Multi-Stage Feature Fusion
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Solution Overview
Problem
Current 3D target detection methods based on geometric constraints or semantic knowledge suffer from low detection accuracy in automatic driving scenarios.
Innovation Solution
A target detection method that performs feature extraction and position coding on images to obtain multi-stage image features and position codes, which are then used to improve detection accuracy by fusing detection results across stages and considering uncertainties through attention networks and Intersection-over-Union calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If 3D target detection is performed based on geometric constraints or semantic knowledge, then the detection process can be simplified, but the detection accuracy becomes low
Solution Approach 1:
The detection process is divided into multiple stages with different feature extraction depths. Early stages use shallow features for quick detection while later stages use deep features for accurate detection, allowing the system to balance complexity and accuracy across different detection scenarios
Solution Approach 2:
The patent introduces multi-stage feature extraction that adds a temporal dimension to the detection process. By processing features through multiple stages with increasing complexity, the system achieves high accuracy without requiring all complex processing for every detection case
2Measurement precision
If multi-stage feature extraction is performed to improve detection accuracy, then the detection precision increases, but the computational complexity increases
Solution Approach 1:
The computational workload is segmented across multiple stages, with each stage processing features at different levels of abstraction. This allows the system to distribute computational complexity rather than concentrating it all in one stage, improving accuracy while managing overall computational load
Solution Approach 2:
Early stages perform preliminary feature extraction and filtering, preparing data for later stages. This preliminary action reduces the burden on subsequent stages by pre-processing and organizing features, thereby improving overall efficiency despite multiple stages
Data Source
AI summary
The present disclosure provides a target detection and model training method and apparatus, a device and a storage medium, and relates to the field of artificial intelligence, and in particular, to computer vision and deep learning technologies, which may be applied to smart city and intelligent transportation scenarios. The target detection method includes: performing feature extraction processing on an image to obtain image features of a plurality of stages of the image; performing position coding processing on the image to obtain a position code of the image; obtaining detection results of the plurality of stages of a target in the image based on the image features of the plurality of stages and the position code; and obtaining a target detection result based on the detection results of the plurality of stages.