Stereo Camera Anomaly Detection Using Depth Maps in Farm Fields
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Solution Overview
Problem
Existing agricultural vehicle anomaly detection systems struggle with accuracy, cost, and complexity, particularly in handling dynamic and complex agricultural environments, and fail to consider spatial distributions and relationships between anomalies.
Innovation Solution
An agricultural vehicle equipped with multiple stereo cameras and an anomaly detection system that applies deep neural networks to rectified image data, generating combined anomaly predictions and controlling operations based on depth maps and spatial distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If LiDAR, GNSS, and RADAR units are used for anomaly detection, then some environmental information is provided, but accuracy and suitability for detecting small objects, thin obstacles, or irregularly shaped features deteriorates
Solution Approach 1:
The patent replaces traditional sensor-based anomaly detection systems (LiDAR, GNSS, RADAR) with a camera-based deep learning system. The mechanical/optical sensing approach is substituted with computational image analysis using convolutional neural networks, which are better suited for detecting visual anomalies like small objects, thin obstacles, and irregularly shaped features in agricultural environments.
Solution Approach 2:
The patent changes the detection parameters by using deep learning models that can identify anomalies based on visual patterns rather than physical measurements. The system transforms the detection approach from measuring distance and position (traditional sensors) to analyzing image features, textures, and patterns (camera-based deep learning), thereby improving detection accuracy for various anomaly types.
2Loss of information
If camera-based anomaly detectors are used, then rich visual information is captured in a cost-effective way, but difficulty in handling varying lighting conditions, occlusions, and clutter increases
Solution Approach 1:
The patent introduces deep learning models as an intermediary between the camera input and anomaly detection output. These models serve as a mediator that can interpret complex visual scenes, handling variations in lighting, occlusions, and clutter by learning robust feature representations from training data, thereby reducing the difficulty of detecting anomalies under challenging conditions.
Solution Approach 2:
The patent applies preliminary action by pre-training deep learning models on extensive datasets of agricultural images with various lighting conditions, occlusions, and clutter. This preliminary training enables the models to learn invariant features and robust detection capabilities before actual anomaly detection occurs, reducing the difficulty of handling challenging environmental conditions during operation.
3Device complexity
If traditional image processing techniques or simple machine learning models are used, then system complexity is reduced, but robust and accurate detection of a wide range of anomalies deteriorates
Solution Approach 1:
The patent substitutes traditional image processing techniques and simple machine learning models with deep learning-based anomaly detection systems. This replacement increases computational complexity but dramatically improves detection accuracy and robustness across a wide range of anomaly types in complex agricultural settings.
Solution Approach 2:
The patent employs a composite approach by combining multiple deep learning components (feature extraction networks, anomaly detection models, confidence scoring systems) into an integrated anomaly detection system. This composite structure enables robust and accurate detection of diverse anomalies while managing complexity through modular architecture.
4Reliability
If existing anomaly detection systems are used, then basic detection capability is provided, but ability to consider spatial distributions and relationships between anomalies deteriorates
Solution Approach 1:
The patent adds the spatial dimension to anomaly detection by using deep learning models that process not only the presence but also the spatial distribution and relationships between anomalies. The system analyzes spatial patterns, distances, and configurations of detected anomalies, providing a more comprehensive understanding of the agricultural environment beyond basic detection.
Data Source
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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.