Multi-Level Domain Adaptive Object Detection for Autonomous Driving
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Solution Overview
Problem
Existing object detection technologies face performance deterioration due to the domain gap between training data and real-world environments, particularly in autonomous driving, where data collection is limited and costly, and current domain adaptation methods overlook the interrelations among object detector stages.
Innovation Solution
A multi-level transferable region-based domain adaptive object detection approach that includes an image-level feature part, an RPN-level feature part, and an instance-level feature part, which extract and fuse multi-scale features, perform domain adaptation, and adjust region candidates based on foreground probability and image hardness scores, using convolutional layers, region proposal networks, and deformable convolutional networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain adaptation is conducted intensively on transferable regions in the feature extraction stage only, then the domain gap is reduced in that stage, but the interrelations among object detector stages are overlooked causing negative domain transfer
Solution Approach 1:
The patent segments the domain adaptation process into three distinct stages: image-level feature extraction, RPN-level region proposal, and instance-level feature classification. Each stage has its own domain adaptation module that processes features at the appropriate level, allowing comprehensive domain gap reduction across the entire object detection pipeline while maintaining modular architecture.
Solution Approach 2:
The patent introduces multi-level domain adaptation by adding vertical dimensionality to the feature processing pipeline. Instead of applying domain adaptation only at the image level, it extends domain adaptation operations across multiple hierarchical levels (image-level, RPN-level, instance-level), creating a multi-dimensional approach that captures interrelations among stages.
2Adaptability or versatility
If data collection is conducted through car users all over the world, then diverse real-world data is obtained, but economical and practical limitations arise
Solution Approach 1:
The patent uses simulator-generated data as a copy or synthetic representation of real-world driving scenarios. By creating virtual training data that mimics diverse environmental conditions (different weather, lighting, geography), the system achieves broad environmental adaptability without the prohibitive costs of collecting actual data from vehicles worldwide.
Solution Approach 2:
The patent employs domain adaptation techniques that transform features by changing their statistical parameters and distributions. By adjusting feature representations through domain adaptation modules at multiple levels, the system adapts to diverse environments using data from limited sources, eliminating the need for extensive data collection across various geographical locations.
3Measurement precision
If object detectors are trained on large-scale data, then detection performance improves, but performance deteriorates due to domain gap between training data and actual environment
Solution Approach 1:
The patent applies domain adaptation operations preliminarily at each processing stage before features are passed to the next stage. By reducing the domain gap at the image-level feature extraction stage, then again at the RPN-level region proposal stage, and finally at the instance-level classification stage, the system proactively prevents domain shift accumulation and maintains detection precision across domains.
Solution Approach 2:
The patent implements feedback mechanisms where domain adaptation modules receive features from previous stages, apply domain-invariant transformations, and feed adapted features to subsequent stages. This continuous feedback loop ensures that domain gap reduction is systematically applied throughout the pipeline, maintaining both detection precision and robustness to domain variations.
Data Source
AI summary
A multi-level transferable region-based domain adaptive object detection apparatus includes: an image-level feature part that extracts an image-level feature by providing unlabeled target data to a plurality of convolutional layers; an RPN-level feature part that determines region candidates by providing the image-level feature to a region proposal network (RPN) and performing RPN-level domain adaptation and transferable region filtering; and an instance-level feature part that extracts a region of interest (RoI) pooling feature by conducting dynamic instance sampling on the region candidates.