Stereo Camera Anomaly Detection for Agricultural Vehicle Navigation
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Solution Overview
Problem
Existing anomaly detection systems in agricultural vehicles face challenges in accurately and robustly detecting a wide range of anomalies, particularly in complex agricultural settings, due to limitations in sensor accuracy, cost, and complexity, and fail to consider spatial distributions and relationships between anomalies, especially when the vehicle and anomalies are moving.
Innovation Solution
An agricultural vehicle equipped with multiple stereo cameras and an anomaly detection system that applies deep neural networks to rectified image data from each camera, generating combined anomaly predictions and controlling operations based on depth maps and spatial distributions to enhance detection accuracy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LiDAR units, GNSS units, and/or RADAR units are used for anomaly detection, then some information about the environment can be provided, but accuracy, cost, and complexity are limited
Solution Approach 1:
The patent replaces complex mechanical sensor systems (LiDAR, RADAR, GNSS) with an optical system using stereo cameras. This substitution maintains anomaly detection capability while reducing system complexity and cost. The stereo camera system captures visual information that is processed through rectification and disparity mapping to achieve reliable anomaly detection without the complexity of active sensing systems.
Solution Approach 2:
The patent uses stereo cameras to capture visual copies of the environment from multiple perspectives. By rectifying images from both cameras and creating disparity maps, the system generates accurate spatial representations of anomalies. This copying approach allows reliable detection of small objects, thin obstacles, and irregularly shaped features without requiring complex mechanical sensors.
2Reliability
If camera-based anomaly detectors are used, then rich visual information can be captured in a cost-effective way, but difficulty in handling varying lighting conditions, occlusions, and clutter remains
Solution Approach 1:
The patent transitions from 2D image analysis to 3D spatial understanding by utilizing stereo vision. By rectifying images from both cameras and computing disparity maps, the system adds the depth dimension, enabling robust anomaly detection that is invariant to lighting conditions, occlusions, and clutter. The 3D spatial relationships provide additional information that helps distinguish anomalies from background variations.
Solution Approach 2:
The patent introduces rectified image data and disparity maps as intermediary representations between the raw camera images and anomaly detection. This intermediary processing step transforms the challenging visual data into a standardized 3D spatial format that is more robust to varying lighting conditions, occlusions, and clutter, making anomaly detection more reliable.
3Measurement precision
If traditional image processing techniques or simple machine learning models are used, then processing is simpler, but robust and accurate detection of a wide range of anomalies fails
Solution Approach 1:
The patent employs a dynamic anomaly detection system that adapts to different anomaly types and environmental conditions. The system processes rectified images from stereo cameras, generates disparity maps, and uses machine learning models that can dynamically adjust to detect various anomalies including small objects, thin obstacles, and irregularly shaped features. This dynamic approach achieves high detection accuracy while managing processing complexity through efficient stereo vision algorithms.
Solution Approach 2:
The patent combines multiple processing components (stereo rectification, disparity mapping, machine learning anomaly detection) into a composite system. This composite approach integrates the strengths of each component to achieve robust and accurate anomaly detection across diverse conditions, overcoming the limitations of simple machine learning models while managing overall system complexity.
4Measurement precision
If single-camera systems are used, then system simplicity is maintained, but spatial distributions and relationships between anomalies cannot be considered
Solution Approach 1:
The patent adds the depth dimension by using stereo camera pairs instead of single cameras. By rectifying images from both cameras in each stereo pair and computing disparity maps, the system achieves accurate 3D spatial understanding of anomalies. This dimensional enhancement enables detection of spatial distributions and relationships between anomalies while maintaining manageable system complexity through standardized stereo vision 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.