Camera System Model Selection Using Accuracy Feedback
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Solution Overview
Problem
Conventional detection devices using AI models for object detection in images struggle with fluctuating accuracy due to environmental variations, often failing to select the most accurate model for the current conditions.
Innovation Solution
A detection device that evaluates multiple AI models by calculating detection accuracy for each and selects the model with the highest average accuracy for subsequent use, allowing for dynamic switching based on environmental conditions and time slots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If an AI model is selected based on surrounding environment, then the system can adapt to environmental changes, but the detection accuracy may fluctuate and not always achieve high accuracy
Solution Approach 1:
The system calculates detection accuracy for each AI model by comparing detected objects with ground truth data, uses this feedback to determine which model has the highest accuracy, and selects that model for subsequent detection tasks. This closed-loop feedback mechanism ensures the system continuously improves detection accuracy while adapting to environmental changes.
Solution Approach 2:
The system changes the selection parameter from environmental conditions to detection accuracy metrics. Instead of selecting models based on environmental factors like time of day or lighting conditions, the system selects models based on their actual performance measured by detection accuracy calculations, thereby resolving the contradiction between adaptability and precision.
2Adaptability or versatility
If multiple AI models are used to handle environmental variations, then the system can maintain detection capability across different conditions, but the complexity of model management increases
Solution Approach 1:
The system automatically calculates detection accuracy for each model, compares the accuracy values, and selects the highest-accuracy model without requiring manual intervention or complex management logic. The self-service mechanism simplifies model management by letting the system autonomously determine the best model based on objective accuracy metrics.
Solution Approach 2:
The system segments the model management process into distinct steps: calculating accuracy for each model, comparing accuracy values, and selecting the best model. This segmentation of the management process reduces complexity by breaking down the overall task into simple, manageable operations that can be executed automatically.
Data Source
AI summary
A detection device includes a detection processing unit to execute a detection process of using a learned model selected from a plurality of learned models, using an image as an input to the selected learned model, and obtaining a detection result, as a result of detecting an object in the image, as an output from the selected learned model and a model control unit to execute a determination process of having the detection process executed in regard to each of the plurality of learned models, calculating accuracy of the detection result in regard to each of the plurality of learned models, and determining a recommended learned model out of the plurality of learned models based on the accuracy. The detection process after the determination process is executed by using the recommended learned model.