Animal Detection System Using Zone-Based Repellent Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional home security systems fail to effectively differentiate between wild animals and pets or humans, leading to unnecessary activation of repellent mechanisms and potential collateral damage, and often use universal deterrence strategies that may not be effective for all animal types.
Innovation Solution
An electronic apparatus that uses image-capture devices and machine learning algorithms to detect and classify animals, allowing for targeted activation of repellent mechanisms only in specific zones selected by the user, based on the type of animal and its location, minimizing harm to pets, humans, and property.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If universal deterrence strategies are used for all animal detections, then the repellent mechanism is activated more frequently, but this causes unnecessary collateral damage to pets and humans
Solution Approach 1:
The system applies different treatment strategies to different types of detected objects. Pet animals are identified and excluded from repellent activation, while wild animals trigger the repellent mechanism. This localized differentiation resolves the contradiction by making the deterrence strategy specific to the target object type rather than universal.
Solution Approach 2:
The detection system segments animals into different categories (pets vs. wild animals) based on visual recognition. This segmentation allows the system to apply appropriate responses to each category, activating repellent only for wild animals while ignoring pets, thereby eliminating unnecessary collateral damage while maintaining effective deterrence.
2Reliability
If repellent mechanisms are activated for all detected animals, then animal intrusion is deterred, but pets and humans may be harmed
Solution Approach 1:
The visual recognition system acts as an intermediary between animal detection and repellent activation. It processes the detected object and determines whether it is a pet or wild animal, serving as a mediator that filters which detections should trigger the repellent mechanism, thereby preventing harm to pets and humans while maintaining intrusion detection accuracy.
3Object-affected harmful factors
If zone-based targeted activation is implemented, then collateral damage is reduced, but the system complexity increases
Solution Approach 1:
The system performs preliminary classification of detected animals using visual recognition before activating the repellent mechanism. This preliminary action of identifying pet animals and excluding them from repellent activation simplifies the overall system logic compared to complex zone-based geometric calculations, while still achieving the goal of reducing collateral damage.
Data Source
AI summary
An electronic apparatus and a method are provided for detection of animal intrusions and control of a repellent mechanism to prevent such intrusions. The electronic apparatus controls an image-capture device to acquire an image of a house yard and selects a set of zones in the acquired image based on a user-specified setting. The electronic apparatus detects an animal in the acquired image and controls the repellent mechanism based on a determination that the detected animal is in a zone which is among the selected set of zones, to target the detected animal.