Stereo Camera Anomaly Detection for Small Obstacles in Farm Vehicles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing anomaly detection systems in agricultural vehicles struggle with accuracy, cost, and complexity, particularly in handling small objects, thin obstacles, and irregularly shaped features in complex agricultural environments, and fail to consider spatial distributions and relationships between moving anomalies.
Innovation Solution
Implementing a method that utilizes two stereo cameras with separate anomaly detection deep neural networks to generate and combine anomaly predictions, incorporating pixel-wise combination and priority-based mask operations, and generating depth maps to control agricultural vehicle operations based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional image processing techniques or simple machine learning models are used for anomaly detection, then the system complexity and cost are reduced, but the detection accuracy and robustness deteriorate in complex agricultural settings
Solution Approach 1:
The patent transforms the anomaly detection approach by changing the fundamental parameter of the detection model from traditional image processing or simple machine learning to deep learning neural networks. This parameter change enables the system to achieve high detection accuracy for small objects, thin obstacles, and irregularly shaped features while maintaining reasonable system complexity through efficient network architectures.
Solution Approach 2:
The patent replaces traditional mechanical image processing techniques with intelligent deep learning systems. The neural networks automatically learn and extract features from images, substituting manual feature engineering and traditional processing algorithms with adaptive, data-driven models that achieve superior detection performance in complex agricultural environments.
2Reliability
If LiDAR units, GNSS units, and RADAR units are used for anomaly detection, then the detection coverage is improved, but the cost and device complexity increase
Solution Approach 1:
The patent extracts and focuses on the most effective and cost-efficient sensor modality for agricultural anomaly detection. By selecting camera-based systems and eliminating the need for expensive LiDAR, GNSS, and RADAR units, the invention achieves reliable detection coverage for agricultural anomalies while significantly reducing system complexity and cost.
Solution Approach 2:
The patent employs cost-effective camera sensors instead of expensive active sensing systems like LiDAR and RADAR. Camera-based anomaly detection provides sufficient coverage for agricultural applications at a fraction of the cost, making the system economically viable while maintaining reliable detection performance.
3Device complexity
If camera-based anomaly detectors are used, then the cost is reduced and visual information quality is improved, but the ability to handle varying lighting conditions, occlusions, and clutter deteriorates
Solution Approach 1:
The patent changes the processing parameters of camera-based detection by implementing deep learning neural networks that are specifically designed to handle varying lighting conditions, occlusions, and clutter. These networks learn robust feature representations that are invariant to environmental variations, enabling reliable anomaly detection across diverse agricultural settings while maintaining cost-effectiveness.
Solution Approach 2:
The patent performs preliminary training of deep learning models on extensive datasets that include various lighting conditions, occlusions, and cluttered agricultural scenes. This preliminary action prepares the system to handle challenging conditions during actual operation, enabling camera-based detectors to overcome their traditional weaknesses without increasing hardware complexity.
4Ease of operation
If existing anomaly detection systems are used, then the basic detection function is provided, but the ability to detect small objects, thin obstacles, and irregularly shaped features deteriorates
Solution Approach 1:
The patent changes the detection parameters by employing deep learning neural networks with architectures optimized for detecting small objects, thin obstacles, and irregularly shaped features. These networks use multi-scale feature extraction and attention mechanisms that significantly improve detection precision for challenging anomaly types while maintaining ease of operation through automated processing.
Data Source
AI summary
An agricultural vehicle includes multiple stereo cameras operably coupled to the agricultural vehicle, and an anomaly detection system that receives image data from the stereo cameras. The anomaly detection system operates on a computing device including at least one processor, and instructions that cause the processor to receive the image data from the multiple stereo cameras, utilize advanced machine learning model techniques to detect anomaly predictions in an agricultural field surrounding the agricultural vehicle, and control operations of the agricultural vehicle based on the anomaly predictions. Related agricultural vehicles and methods are also disclosed.