Object Detection Apparatus Using Dynamic Recognition Dictionary Selection
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Solution Overview
Problem
Existing object detection systems using image recognition dictionaries face performance degradation when environmental conditions, such as distance, brightness, contrast, and color, deviate from assumed conditions, leading to unstable detection performance.
Innovation Solution
An object detection apparatus that stores multiple recognition dictionaries and algorithms, allowing for the selection of optimal combinations based on specified distance and light conditions to perform image recognition, thereby enhancing detection reliability across varying environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single recognition dictionary is used, then the device complexity is low, but the detection reliability degrades when environmental conditions vary
Solution Approach 1:
The system dynamically selects different recognition dictionaries based on detected environmental conditions (distance, brightness, contrast, color). The selection means switches between multiple pre-stored recognition dictionaries depending on the current environmental state, allowing the system to adapt to varying conditions while maintaining manageable complexity through condition-based selection rather than processing all possibilities simultaneously
Solution Approach 2:
The system changes the parameter of recognition dictionary selection based on environmental condition parameters (distance, brightness, contrast, color). By monitoring these environmental parameters and selecting appropriate recognition dictionaries accordingly, the system maintains high detection reliability across different conditions without requiring a single overly complex dictionary to handle all scenarios
2Adaptability or versatility
If multiple recognition dictionaries are stored, then the adaptability to environmental conditions improves, but the device complexity increases
Solution Approach 1:
The system segments the recognition task by dividing the environmental condition space into different categories (e.g., distance ranges, lighting conditions) and storing separate recognition dictionaries for each segment. This allows the system to handle diverse environmental conditions with specialized dictionaries while maintaining overall system manageability through organized segmentation of the recognition space
Solution Approach 2:
Multiple recognition dictionaries are prepared in advance for different environmental conditions before actual detection occurs. The selection means then chooses the appropriate pre-prepared dictionary based on current conditions, avoiding the need to process all possible conditions simultaneously and reducing computational complexity while maintaining broad adaptability
3Reliability
If the recognition dictionary is optimized for assumed conditions, then the manufacturing precision of the recognition system is high, but the reliability decreases when actual conditions differ from assumptions
Solution Approach 1:
The system achieves multi-functionality by storing multiple recognition dictionaries that can handle different environmental conditions. Instead of optimizing a single dictionary for assumed conditions, the system maintains a universal set of dictionaries that can adapt to various conditions (distance, brightness, contrast, color), ensuring reliable detection across diverse scenarios while preserving the optimization benefits of condition-specific dictionaries
Data Source
AI summary
An object detection apparatus includes a storage section storing a plurality of selection patterns as combinations of one of a plurality of recognition dictionaries and one of a plurality of image recognition algorithms, a specifying means for specifying at least one of a distance from a position at which an input image is taken and a target corresponding to the detection object within the input image and a state of light of the input image, a selection means for selecting one from the plurality of the selection patterns based on at least one of the distance and the state of the light specified by the specifying means, and a detection means for detecting the detection object within the input image by performing an image recognition process using the image recognition dictionary and the image recognition algorithm included in the selection pattern selected by the selection means.