Stereo Camera Anomaly Detection for Occluded Agricultural Fields
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Solution Overview
Problem
Existing anomaly detection systems in agricultural vehicles face limitations in accuracy, cost, and complexity, particularly in detecting small objects and irregularly shaped features in complex agricultural settings, and struggle to handle varying lighting conditions and occlusions, while failing to consider spatial distributions and relationships between moving anomalies.
Innovation Solution
An agricultural vehicle equipped with multiple stereo cameras and anomaly detection deep neural networks, where each camera is associated with a unique neural network, generates and combines anomaly predictions to enhance detection accuracy and control operations based on spatial distributions and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LiDAR, GNSS, and RADAR units are used for anomaly detection, then some environmental information is provided, but accuracy and detection capability for small objects and irregular features deteriorate
Solution Approach 1:
The patent replaces traditional sensor-based anomaly detection systems (LiDAR, RADAR, GNSS) with a camera-based deep learning system. The mechanical/optical sensing approach is substituted with computational image analysis using convolutional neural networks, enabling better detection of small objects and irregular features through pattern recognition rather than physical measurement
Solution Approach 2:
The patent introduces camera images as an intermediary between the physical anomaly and the detection system. Instead of directly measuring physical properties with sensors, the system captures visual information through cameras and processes it through deep neural networks, allowing for more nuanced detection of complex anomalies
2Ease of manufacture
If camera-based anomaly detectors are used, then cost is reduced and visual information is captured, but handling varying lighting conditions and occlusions deteriorates
Solution Approach 1:
The patent employs multiple cameras positioned at different locations on the agricultural vehicle, creating a dynamic multi-perspective detection system. As the vehicle moves, different cameras capture anomalies from various angles, allowing the system to handle occlusions and varying lighting conditions by selecting or combining views where the anomaly is most visible
Solution Approach 2:
The deep neural network system processes images from multiple cameras and uses the combined information to validate anomaly detections. The system provides feedback by comparing detections across multiple views and lighting conditions, filtering out false positives caused by occlusions or poor lighting in any single camera view
3Device complexity
If traditional image processing techniques are used, then system complexity is reduced, but detection accuracy for wide range of anomalies deteriorates
Solution Approach 1:
The patent transforms the detection approach by changing from traditional image processing parameters (edges, gradients, basic features) to deep learning parameters (feature representations learned by convolutional neural networks). This parameter transformation enables the system to detect a wide range of anomaly types with high accuracy while the modular architecture keeps implementation manageable
4Device complexity
If single camera anomaly detection is used, then system complexity is reduced, but spatial distribution and relationship analysis of moving anomalies deteriorates
Solution Approach 1:
The patent adds the dimension of multiple camera perspectives and temporal sequences. By capturing images from multiple cameras simultaneously and processing them through deep neural networks, the system recovers spatial distribution information and relationships between moving anomalies that would be lost in a single-camera system
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.