Lateral Camera Image Stitching for Distortion-Aware Vehicle Detection
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Solution Overview
Problem
Current object detection frameworks for autonomous vehicles are inadequate for lateral object detection due to distortion in images from laterally-facing cameras, leading to inefficient and inaccurate detection of objects, which can result in malfunction and safety issues.
Innovation Solution
An autonomous lateral vehicle detection system that receives and warps lateral image data from laterally-facing cameras, aligns objects with the vehicle's orientation, and applies bounding boxes to accurately represent detected objects, while stitching images from multiple cameras to create a unified view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If two-dimensional bounding boxes are used for object detection in lateral camera images, then the detection process is simple, but the detection accuracy deteriorates due to image distortion
Solution Approach 1:
The patent applies image warping to lateral camera images before performing object detection. This preliminary transformation corrects the distortion caused by the lateral camera's angled view, ensuring that objects appear in their true shape and orientation. By pre-processing the images to eliminate distortion, the system enables accurate bounding box detection without requiring complex alternative detection methods
Solution Approach 2:
The patent transforms the imaging parameters by warping the lateral camera images to change their geometric properties. This parameter transformation converts distorted images into undistorted representations, allowing standard object detection algorithms to work effectively on previously undetectable lateral objects
2Adaptability or versatility
If multiple laterally-facing cameras are used to improve detection coverage, then the detection coverage is improved, but the image processing complexity increases
Solution Approach 1:
The patent stitches together images from multiple laterally-facing cameras to create a unified panoramic view. By merging the distorted images from different camera angles into a single corrected panorama, the system achieves comprehensive lateral coverage while simplifying subsequent object detection to a single processed image rather than multiple separate analyses
3Productivity
If bounding boxes are applied directly to distorted lateral images, then the processing speed is fast, but the object representation accuracy deteriorates
Solution Approach 1:
The patent performs image warping as a preliminary step before applying bounding boxes. This pre-correction of distortion ensures that when bounding boxes are applied, they accurately represent the true size, shape, and position of objects. The warping transformation is computed once and applied to the entire image set, maintaining processing efficiency while dramatically improving measurement accuracy
Data Source
AI summary
A system and method for lateral vehicle detection is disclosed. A particular embodiment can be configured to: receive lateral image data from at least one laterally-facing camera associated with an autonomous vehicle; warp the lateral image data based on a line parallel to a side of the autonomous vehicle; perform object extraction on the warped lateral image data to identify extracted objects in the warped lateral image data; and apply bounding boxes around the extracted objects.