Dual Machine Learning Model Object Detection Error Notification

The patent applies universality by designing a verification mechanism that can detect multiple types of erroneous detections (insect flights, weather conditions, lighting changes) using a single general-purpose approach. Instead of creating separate verification systems for each error type, the invention uses a universal verification model that checks detection results against multiple criteria simultaneously, thereby improving reliability without proportionally increasing complexity.

The patent introduces an intermediary verification model that acts as a mediator between the original detection model and the final detection output. This verification model receives detection results from the first model and evaluates them against multiple conditions (whiteout detection, region characteristics, temporal consistency) to determine whether erroneous detections should be filtered out, thus improving reliability while maintaining system manageability.

The patent implements universality by creating a single verification mechanism that handles multiple error types (insect flights, weather-related errors, lighting condition errors) through common evaluation criteria. The verification model uses universal checks such as whiteout detection, region characteristic analysis, and temporal consistency validation that apply across different error scenarios, thereby achieving high detection precision without the complexity of separate implementations for each error factor.

The patent applies parameter changes by dynamically adjusting verification criteria based on detected conditions. The system monitors parameters such as light intensity, region characteristics, and temporal patterns to determine which verification rules to apply. This allows the system to maintain high precision for different error types by adapting verification parameters to current conditions rather than using fixed, complex error-specific implementations.