Object Detection Model Using Environment Information
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Solution Overview
Problem
Existing object detection techniques using machine learning fail to accurately detect objects in images due to the lack of consideration for complex environments, varying appearances of target objects, and different types of objects.
Innovation Solution
The proposed solution involves a learning apparatus and method that acquires environment information from images and uses this information to train an object detection model, enabling the detection of target objects with improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object detection techniques are used, then the detection process is simple, but the detection accuracy is reduced due to inability to consider complicated environment, different appearances, and variation of target objects
Solution Approach 1:
The patent segments the object detection process into multiple specialized modules: environment information acquisition unit, feature extraction unit, and detection unit. Each module handles specific aspects (environmental context, object features, detection logic) independently, allowing the system to consider complicated environments and object variations without creating a monolithic complex system. This modular segmentation enables improved detection accuracy while managing complexity through functional decomposition.
Solution Approach 2:
The patent introduces environment information as an additional dimension of data input beyond traditional image data. By acquiring and processing environment information (such as spatial context, temporal context, or external sensor data) alongside image features, the system expands the detection space from two-dimensional image analysis to multi-dimensional analysis, thereby improving detection accuracy for objects with different appearances and variations.
2Measurement precision
If environment information is acquired and used for training, then detection accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs environment information acquisition and feature extraction during the learning/training phase in advance. The object detection model is pre-trained with environment information, so that during actual detection operations, the model can directly apply the learned knowledge without requiring real-time environment information processing. This preliminary action transfers computational burden from runtime to training time, improving detection accuracy while minimizing processing time during actual use.
3Measurement precision
If the object detection model considers multiple factors (environment, appearance, type variation), then detection accuracy improves, but the model complexity increases
Solution Approach 1:
The patent designs a universal object detection model that can handle multiple detection tasks and object types through a single integrated architecture. The model incorporates environment information processing capabilities that are applicable across different object categories and environmental conditions, rather than requiring separate specialized models for each case. This multi-functionality allows the model to consider environment, appearance, and type variation universally, improving detection accuracy across diverse scenarios while avoiding the complexity of maintaining multiple separate models.
Data Source
AI summary
A learning apparatus includes an environment information acquisition unit and a learning unit. The environment information acquisition unit acquires environment information concerning a learning image. The learning unit detects an object detection model that detects each target object included in the learning image by using the environment information.