ROI-Based Object Recognition for Faster Autonomous Driving Inference
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Solution Overview
Problem
Current object recognition systems for autonomous driving face inefficiencies in processing large images, leading to prolonged inference times due to the high number of object candidate regions, which hinders real-time navigation and control of autonomous vehicles.
Innovation Solution
The implementation of a faster Region-based Convolutional Neural Network (R-CNN) with a Region Proposal Network (RPN) and a processor that extracts a road region of interest (ROI) to reduce the number of object candidate regions, enhancing processing speed by performing operations in parallel or sequentially, utilizing scene segmentation algorithms to determine the number of candidate regions based on the ROI size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system processes all regions of a large input image to ensure comprehensive object detection, then the detection coverage is improved, but the inference time increases significantly
Solution Approach 1:
The patent divides the input image into multiple regions of interest (ROIs) based on scene segmentation, focusing computational resources on areas containing objects of interest rather than processing the entire image uniformly. This segmentation approach maintains detection coverage while reducing the total number of regions requiring detailed processing.
Solution Approach 2:
The system applies different processing strategies to different regions of the image based on their importance. Regions containing objects of interest receive detailed processing with multiple candidate regions generated, while less important regions receive simplified processing. This local quality differentiation improves inference speed without compromising detection reliability in critical areas.
2Measurement precision
If the system generates a high number of object candidate regions to improve detection accuracy, then the recognition precision is improved, but the processing complexity increases
Solution Approach 1:
The system performs preliminary scene segmentation to identify regions containing objects of interest before generating object candidate regions. By pre-identifying relevant areas, the system reduces the total number of candidate regions that need to be processed while ensuring that all actual objects are captured. This preliminary action maintains detection precision while reducing processing complexity.
3Measurement precision
If the system processes large input images with high resolution to improve object detection accuracy, then the detection precision is improved, but the computational load increases
Solution Approach 1:
The patent extracts and processes only the relevant regions of interest from the large input image, rather than processing the entire high-resolution image. By extracting ROIs containing objects of interest and processing these smaller regions at high resolution, the system maintains detection accuracy while significantly reducing the computational load associated with processing the full image.
Data Source
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AI summary
Methods and apparatus for recognizing an object are provided, including extracting a feature from an input image and generating a feature map in a neural network. In parallel with the generating of the feature map, a region of interest (ROI) corresponding to an object of interest is extracted from the input image, and a number of object candidate regions used to detect the object of interest is determined based on a size of the ROI. The object of interest is recognized from the ROI based on the number of object candidate regions in the neural network.