Road Image ROI Extraction for Efficient Long-Distance Object Detection
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Solution Overview
Problem
Existing object detection methods in autonomous driving and robot-following scenarios suffer from low accuracy and efficiency.
Innovation Solution
A method and apparatus that extracts a region of interest (ROI) from an original image, focusing on the pixel point farthest from the sensor, and performs object detection within this ROI, utilizing road segmentation and depth information to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If object detection is performed on the entire original image, then comprehensive detection coverage is achieved, but computing resources are wasted and efficiency is reduced
Solution Approach 1:
The patent divides the original image into multiple regions of interest (ROIs) based on road segmentation results and depth information. Instead of processing the entire image, the system segments it into relevant areas where objects are likely to be detected, thereby reducing computational load while maintaining detection effectiveness.
Solution Approach 2:
The patent extracts specific regions of interest from the original image based on depth information and road segmentation. By taking out only the relevant portions of the image where objects are likely to exist, the system avoids processing unnecessary areas, thus improving efficiency and reducing energy consumption.
2Measurement precision
If object detection is performed on the entire original image, then all potential objects are detected, but detection accuracy for distant objects is reduced
Solution Approach 1:
The patent applies different processing qualities to different regions of the image based on depth information. Regions with greater depth (distant areas) are processed with higher quality and more focused attention, while closer regions use standard processing. This local quality adjustment improves detection accuracy for distant objects without unnecessarily processing the entire image at high quality.
Solution Approach 2:
The patent introduces depth information as an additional dimension for region selection. By using depth maps and depth-based filtering, the system identifies and focuses on distant regions that require higher detection accuracy, adding a depth dimension to the traditional 2D image processing approach.
3Reliability
If the ROI extraction includes all pixel points exceeding a preset distance, then comprehensive long-distance detection is achieved, but processing complexity increases
Solution Approach 1:
The patent performs preliminary road segmentation and depth-based filtering before object detection. By pre-identifying regions that exceed the preset distance threshold and belong to road surfaces, the system prepares the data in advance, which simplifies the subsequent object detection process and reduces overall processing complexity while maintaining reliable long-distance detection.
Data Source
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AI summary
A method for detecting an object includes obtaining (S101) an original image of a road through a sensor by performing an image capturing operation; extracting (S102) a region of interest (ROI) including a first target pixel point from the original image, wherein the first target pixel point is a pixel point in the original image corresponding to a position on the road that is farthest from the sensor; and generating (SI03) an object detection result by performing object detection on the ROI.