Wildlife Deterrence System Using Dynamic Action Selection
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Solution Overview
Problem
Existing wildlife deterrence systems are inefficient as they often rely on single actions, are not environmentally friendly, and fail to differentiate between various types of wildlife, leading to ineffective deterrence and unnecessary activation during human activities.
Innovation Solution
A system that uses a monitor camera and deep neural network to classify wildlife types and trigger specific deterrence actions, such as sound, light, or inflatable figures, based on the type and effectiveness, while avoiding activation during human presence and optimizing energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single deterrence action is used for all wildlife, then the device complexity is reduced, but the effectiveness of deterrence decreases
Solution Approach 1:
The system dynamically selects and switches between different deterrence actions (sound, light, inflatable figures) based on real-time wildlife classification and detected behavior patterns, transforming a static single-action system into a dynamic multi-action system that adapts to different wildlife types and situations
Solution Approach 2:
The system changes operational parameters by selecting different deterrence modalities (acoustic, optical, mechanical) and adjusting their intensity and duration based on wildlife classification results, allowing the same physical device to deliver varied deterrence approaches
2Reliability
If deterrence actions are continuously activated, then the deterrence effectiveness is maintained, but the energy consumption increases
Solution Approach 1:
The system employs periodic activation of deterrence actions rather than continuous operation, triggering sound, light, or inflatable figure deployment only when wildlife is detected and classified, with activation duration and frequency adjusted based on wildlife response patterns to minimize energy consumption while maintaining effectiveness
Solution Approach 2:
The system uses feedback from wildlife classification results and detected behavior changes to control activation timing and duration of deterrence actions, stopping activation when wildlife leaves or shows signs of deterrence, thereby optimizing energy usage based on real-time effectiveness feedback
3Speed
If deterrence system is highly sensitive to trigger, then the response speed to wildlife is improved, but the false triggers during human activities increase
Solution Approach 1:
The system introduces an intermediary classification layer that processes detection signals before triggering deterrence actions, using deep neural networks to analyze visual and behavioral characteristics of detected objects to distinguish wildlife from humans and other non-target objects, thereby maintaining high response speed while reducing false triggers
4Device complexity
If generic deterrence actions are used for all wildlife types, then the device complexity is reduced, but the adaptability to different wildlife decreases
Solution Approach 1:
The system achieves universality by integrating multiple deterrence modalities (sound speakers, light sources, inflatable figures) into a single platform that can deliver different types of deterrence actions through the same hardware infrastructure, controlled by a unified classification and decision-making system
Data Source
AI summary
Method and system for wild animal deterrence, the method includes obtaining video data by a monitor camera of a home wildlife deterrence system; classifying, based on the obtained video data, an object in the video data as a particular type of a wild animal; selecting an action to perform based on the particular type of the wild animal that the object is classified as; and triggering the action to be performed. The method also includes determining that the particular type of the wild animal matches a label of a candidate action in a set of candidate actions, wherein each candidate action in the set of candidate actions indicates at least one type of wild animal; and in response to determining that the particular type of the wild animal matches a label of the candidate action in the set of candidate actions, selecting the candidate action as the action to perform.