2D Vision Pick-Point Detection Using AI Feature Projection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 2D vision-based object detection methods suffer from low accuracy, require manual work, and have insufficient generalization ability in complex situations.
Innovation Solution
A method utilizing 2D vision data and a deep learning model to sense and detect objects by acquiring feature information, including oriented information, midline points, and segmenting objects, thereby improving detection accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 2D vision-based object detection methods (feature-based or template-based) are used, then the system is simple to implement, but the detection accuracy is low in complicated situations
Solution Approach 1:
The patent replaces traditional mechanical vision processing methods (feature extraction, template matching) with an artificial intelligence-based detection system. The AI model automatically learns and extracts features from images, eliminating the need for manual feature engineering and template definition, thereby achieving high detection accuracy in complex situations without proportionally increasing system complexity
Solution Approach 2:
The patent changes the fundamental parameters of the detection system by transitioning from fixed threshold-based methods to adaptive AI-based classification. The AI model dynamically adjusts detection parameters based on learned patterns from training data, enabling accurate detection across varied and complicated scenarios while maintaining system manageability
2Adaptability or versatility
If traditional 2D vision detection methods are used, then the implementation is straightforward, but the generalization ability is insufficient
Solution Approach 1:
The patent applies preliminary action by implementing an offline training phase where the AI model learns from diverse training datasets before deployment. This pre-learning process enables the system to generalize to new, unseen objects and scenarios without requiring complex runtime adjustments, balancing implementation ease with improved adaptability
Solution Approach 2:
The patent enhances universality by designing an AI-based detection system that can identify multiple types of objects across different categories. The single trained model serves multiple detection functions simultaneously, replacing the need for separate specialized detectors for each object type, thereby improving generalization ability while maintaining implementation simplicity
3Productivity
If manual work is used in traditional detection methods, then the system is easier to debug and adjust, but the processing efficiency is low
Solution Approach 1:
The patent implements self-service by enabling the AI model to automatically perform feature extraction, object identification, and classification without manual intervention. The system autonomously processes images and generates detection results, dramatically improving processing efficiency while maintaining ease of operation through standardized APIs and configuration options for debugging and adjustment
Data Source
AI summary
Disclosed is a method for detecting an object based on a 2D vision technology, which is performed by a computing device. The method may include: acquiring 2D vision data; sensing the object in the acquired 2D vision data; acquiring feature information of the sensed object; and detecting the object by projecting at least some of the acquired feature information onto the 2D vision data.