Camera-Based Animal Detection for Rapid Species-Specific Deterrence
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Solution Overview
Problem
Existing animal deterrent systems rely on motion detection, which is unreliable and prone to false positives and negatives, failing to distinguish between humans and non-human animals, and image recognition methods are slow, complex, and imprecise.
Innovation Solution
Utilizes convolution neural networks and deep learning technology for rapid and accurate animal identification, deploying deterrents within 0.25 to 2 seconds of detection, and incorporates a camera system connected to a computer processing unit for real-time species-specific animal detection and deterrence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion detection systems are used to detect animals, then the system can detect movement, but it produces false positives and negatives and cannot distinguish between humans and animals
Solution Approach 1:
The patent replaces traditional motion detection systems with a camera-based image recognition system using machine learning algorithms. This substitution enables the system to analyze visual features and classify targets as animals or humans, thereby eliminating false positives and negatives while maintaining detection reliability.
Solution Approach 2:
The system changes the detection parameters from simple motion detection to multi-parameter image analysis including shape, size, texture, and behavioral patterns. By analyzing multiple parameters simultaneously, the system achieves both high detection reliability and precise target identification, distinguishing between animals and humans effectively.
2Measurement precision
If traditional image recognition methods are used, then objects can be identified, but the process is slow, complex, and imprecise
Solution Approach 1:
The system performs preliminary training of machine learning models with extensive animal image data before deployment. This preliminary action pre-configures the recognition algorithms, enabling rapid and accurate identification during actual operation without requiring complex real-time processing, thus achieving both speed and precision.
Solution Approach 2:
The patent uses synthetic image data and augmented training samples to create comprehensive training datasets. By copying and transforming existing animal images into varied training examples, the system learns robust recognition patterns that improve identification accuracy while maintaining fast processing speeds during deployment.
3Measurement precision
If deep learning models are trained with extensive data, then identification accuracy improves, but training time and computational resources increase
Solution Approach 1:
The system performs comprehensive model training and optimization during the development phase before deployment. By completing extensive training in advance, the model achieves high identification accuracy while the deployed system operates efficiently with minimal real-time computational overhead, thus reducing perceived training time loss.
Solution Approach 2:
The patent implements dynamic training strategies that adapt to available computational resources. The system can perform incremental learning and fine-tuning based on resource availability, allowing continuous improvement of accuracy without requiring fixed, extensive training periods, thus optimizing the balance between accuracy and training time.
Data Source
AI summary
This disclosure provides a method of detecting and deterring a target animal from a target area. A target area is positioned within the field of vision of a video camera connected to a computer processing system. An animal identification computer program using convolution neural networks and deep learning computer programs and camera images rapidly detects a target animal. The animal identification computer program is trained to identify target animals accurately using a learning algorithm and related machine learning technology. The time to deploy a deterrent against a target animal from the instant of detection is 2 seconds or less so that little or no time is available to the target animal to damage the target area.