Automated Pest Detection Using Spatial Partition Models
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Solution Overview
Problem
Manual inspection of capture areas for pests is inefficient, prone to errors, and often requires technicians to access hard-to-reach locations, leading to delayed responses to pest contamination and undercounting due to background interference and occlusions.
Innovation Solution
A system that enables remote monitoring and automatic inspection of capture areas by receiving images from sensors, removing background and foreground elements, generating spatial models to preserve object sizes and positions, and performing object detection and filtering to generate accurate count statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is performed by technicians, then object detection can be conducted, but it is time-consuming, labor-intensive, and prone to human error
Solution Approach 1:
The patent replaces the mechanical manual inspection system with an automated computer vision system using cameras and machine learning algorithms to detect, classify, and count objects in capture areas, eliminating human labor while improving detection accuracy and enabling real-time monitoring
Solution Approach 2:
The system enables self-service automated inspection where the capture apparatus autonomously performs object detection and counting without requiring technician intervention, with the machine learning model automatically processing images and generating inspection results
2Reliability
If technicians access hard-to-reach places for inspection, then complete coverage is achieved, but it requires ladders and potentially shutting down customer sites
Solution Approach 1:
The capture apparatus performs self-inspection using integrated cameras and sensors, eliminating the need for technicians to physically access difficult locations while maintaining complete inspection coverage of the capture area
Solution Approach 2:
The patent replaces the mechanical access system (technicians using ladders) with an automated optical inspection system that can remotely capture and analyze images of the capture area without physical intervention
3Loss of information
If background elements are present in the capture area, then the scene is complete, but dust specks and debris are mistaken for target objects leading to false positives
Solution Approach 1:
The patent applies local quality by using class-specific detection criteria where different object classes (pests vs. debris) have different characteristic features, allowing the system to distinguish between target objects and background elements based on their unique visual properties
Solution Approach 2:
The machine learning model uses feedback from training data to continuously improve its ability to distinguish target objects from background elements, learning from examples to reduce false positives while maintaining complete scene representation
4Loss of information
If structural elements are present in the capture area, then the apparatus structure is complete, but they partially obscure objects leading to undercounting
Solution Approach 1:
The patent applies segmentation by dividing the image processing into distinct stages: initial object detection, identification of obscured regions, and compensatory detection, allowing the system to systematically handle partially obscured objects without missing counts
Solution Approach 2:
The system transitions from two-dimensional image analysis to three-dimensional spatial reasoning by inferring the presence of obscured objects based on spatial relationships and partial visibility, effectively recovering counts of objects hidden behind structural elements
5Device complexity
If conventional object detection algorithms are used, then simple implementation is achieved, but they mistakenly interpret dust specks or debris as target objects
Solution Approach 1:
The patent replaces simple threshold-based detection algorithms with machine learning-based classification systems that analyze multiple features of detected objects, significantly improving classification accuracy between target pests and background debris
Solution Approach 2:
The system changes detection parameters by using class-specific criteria where different object classes have different characteristic features (size, shape, texture, color), allowing sophisticated discrimination between target objects and false positives while maintaining implementation feasibility
Data Source
AI summary
An image may be received from a sensor associated with a capture area. Background elements associated with the capture area may be removed and a cleaned image generated. Objects may then be detected in this cleaned image. A spatial partition model may be generated to understand z-index and partially obscured areas while preserving relative object sizes. This model may be used to determine and remove foreground elements that may obstruct objects of interest. Object detection may be performed again on the further cleaned image. Detected objects may be filtered based on predefined criteria for different object classes.