Region-Segmented Object Recognition for Distant Objects
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Solution Overview
Problem
Distant objects and poor line-of-sight conditions pose challenges for reliable object detection, which is crucial for ensuring safety in driving scenarios such as expressway driving or adverse weather conditions.
Innovation Solution
An information processing device that includes an object detection unit and an object determination unit, which segments image data into regions representing different classes of objects and determines confidence levels to accurately classify objects, even in overlapping regions, using machine learning models to enhance detection reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object detection is performed using conventional methods, then detection speed is maintained, but detection reliability deteriorates for distant objects and poor line-of-sight conditions
Solution Approach 1:
The image data is segmented into multiple regions (first region detected by object recognition unit, second regions detected by region recognition unit) to enable specialized processing for different areas. This segmentation allows the system to apply different confidence level thresholds and processing strategies to different regions, improving overall detection reliability without uniformly increasing complexity across the entire system.
Solution Approach 2:
The patent introduces a confidence level dimension to the object detection process. By evaluating confidence levels from both object recognition and region recognition, and comparing them against predetermined thresholds, the system adds a decision-making dimension that improves reliability. This dimensional approach allows the system to make more informed detection decisions without fundamentally changing the core detection mechanisms.
2Measurement precision
If multiple recognition units are used to improve detection accuracy, then object classification accuracy improves, but processing time increases
Solution Approach 1:
The system applies region recognition processing selectively rather than uniformly across all objects. By using object recognition for initial detection and applying region recognition primarily to overlapping regions or when confidence levels are insufficient, the system achieves improved accuracy where needed while avoiding unnecessary processing time for clearly detected objects.
Solution Approach 2:
The system uses confidence level feedback from object recognition to determine whether region recognition is necessary. When the object recognition confidence level exceeds a predetermined threshold, region recognition may be skipped. This feedback mechanism allows the system to dynamically adjust processing based on detection quality, balancing accuracy improvements against processing time constraints.
Data Source
AI summary
An object recognition unit detects, from image data that represents an image using respective signal values of a plurality of pixels, a first region that is a region representing the object in the image. The object recognition unit determines a first confidence level that is a class confidence level for a first class, the first class being a class of the object represented in the first region. A region recognition unit segments the image of the image data into second regions representing different classes of an object, and determines, for each of second regions, a second class being a class of an object in the second region. An object determination unit determines, as the second class, a class of an object in a non-overlapping region that is the second regions that do not overlap with the first region.