User-Assisted Object Detection Confidence Adjustment
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Solution Overview
Problem
Conventional machine-learning models for object detection in images face performance gaps due to limited training data for rare classes, high costs and time for labeling data, and environmental changes such as geo-locations and lighting variations, leading to suboptimal performance in real-world deployments.
Innovation Solution
A user-assisted object detection system that utilizes a machine-learning model to receive user input, modify confidence levels of detected objects based on similarity metrics, and update templates for improved detection accuracy, allowing real-time adaptation to new environments and classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning models are trained using large amounts of labeled data, then detection accuracy for common classes improves, but cost and time for data labeling increases significantly
Solution Approach 1:
The system implements a feedback mechanism where users correct detection errors and provide feedback on detected objects. This feedback is used to update the machine-learning model in real-time, allowing the model to learn from actual usage patterns rather than requiring extensive pre-labeling of training data. The feedback loop continuously improves detection accuracy without proportionally increasing data labeling time.
Solution Approach 2:
The system enables self-service learning where the machine-learning model automatically updates its parameters based on user interactions and corrections. Instead of requiring manual retraining with newly labeled data, the model serves itself by incorporating user feedback directly into its detection algorithm, eliminating the need for time-consuming manual data labeling processes.
2Adaptability or versatility
If machine-learning models are trained on diverse training data, then adaptability to different environments improves, but cost and complexity of model training increases
Solution Approach 1:
The system transitions from static pre-trained models to dynamic models that continuously adapt to new environments through real-time user feedback. The machine-learning model adjusts its detection parameters and characteristics based on actual usage patterns in different geo-locations and environmental conditions, enabling adaptability without requiring complex retraining processes for each new environment.
Solution Approach 2:
The system modifies model parameters dynamically based on user feedback and detection performance in different environments. Instead of retraining the entire model with new environmental data, the system adjusts specific parameters and weights of the machine-learning model to adapt to new conditions, reducing training complexity while maintaining adaptability.
3Measurement precision
If machine-learning models are trained for rare classes, then detection capability for specialized objects improves, but training data availability becomes insufficient
Solution Approach 1:
The system uses feedback from users interacting with the model to generate synthetic training data for rare classes. When users correct detections or provide feedback on rare objects, this information is fed back into the system to create targeted training examples, effectively multiplying the available training data for rare classes without requiring actual rare objects to be manually labeled.
Solution Approach 2:
The system creates copies of rare class examples through user feedback and synthetic data generation. Instead of requiring actual images or data of rare objects for training, the system generates virtual copies and variations of rare class examples through algorithmic processes driven by user interactions, expanding the training dataset for rare classes.
4Adaptability or versatility
If machine-learning models are deployed in changing environments, then real-world applicability improves, but model performance degrades due to environmental variations
Solution Approach 1:
The system implements continuous feedback mechanisms that monitor detection performance in real-world environments and automatically adjust the machine-learning model accordingly. Users provide feedback on detection accuracy in varying environmental conditions, and this feedback is used to update model parameters, ensuring performance stability despite environmental changes such as different geo-locations, lighting conditions, and seasonal variations.
Data Source
AI summary
In some examples, systems and methods for user-assisted object detection are provided. For example, a method includes: receiving an input image, and performing object detection by a software detector to identify a set of detected objects. The software detector includes a machine-learning model. The method further includes outputting one or more indicators of the set of detected objects. Each detected object in the set of detected objects is associated with a confidence level. The method further includes receiving a user input; identifying a template including an image portion associated with the user input; determining a similarity metric between the template and an object in the set of detected objects; modifying a confidence level of the object based at least in part on the determined similarity metric; and generating an output including an indicator of the object based at least in part on the modified confidence level.