Agricultural Vehicle Anomaly Tracking With Multi-Sensor Point Clouds
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Solution Overview
Problem
Existing anomaly detection systems in agricultural vehicles struggle with accuracy, cost, and complexity, particularly in handling varying lighting conditions, occlusions, and clutter, and fail to consider spatial distributions and relationships between anomalies in complex agricultural settings, especially when the vehicle and anomalies are moving.
Innovation Solution
Implementing a deep neural network (DNN) that combines data from multiple sensors like cameras, LiDAR, and RADAR to generate anomaly predictions, classify them as static or dynamic, and control vehicle operations based on these predictions using transformer-based models for point-cloud data processing and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LiDAR, GNSS, and RADAR units are used for anomaly detection, then detection capability is provided, but accuracy, cost, and complexity increase
Solution Approach 1:
The patent combines data from multiple sensor types (cameras, LiDAR, RADAR) into a unified point-cloud representation, merging their capabilities to achieve comprehensive anomaly detection while managing system complexity through integrated processing
2Ease of manufacture
If camera-based anomaly detectors are used, then cost is reduced and visual information is captured, but handling varying lighting conditions, occlusions, and clutter becomes difficult
Solution Approach 1:
The patent creates a composite data representation by fusing camera images with LiDAR and RADAR data into a unified point-cloud model, combining the cost-effectiveness of cameras with the robustness of active sensors to handle varying lighting and occlusion conditions
3Device complexity
If traditional image processing techniques are used, then simplicity is maintained, but robust and accurate detection of wide range of anomalies fails
Solution Approach 1:
The patent replaces traditional image processing techniques with a deep learning-based anomaly detection DNN that processes multi-sensor point-cloud data, substituting conventional algorithms with neural network-based detection to achieve robust and accurate anomaly identification across diverse agricultural settings
4Reliability
If existing anomaly detection systems are used, then basic detection is provided, but spatial distributions and relationships between anomalies are not considered
Solution Approach 1:
The patent enhances anomaly detection by incorporating spatial dimensionality through point-cloud data processing, enabling the system to detect and analyze spatial distributions and relationships between multiple anomalies in three-dimensional agricultural environments rather than treating them as isolated two-dimensional detections
Data Source
AI summary
An agricultural vehicle includes multiple sensors operably coupled to the agricultural vehicle, and an anomaly detection system that acquires sensor data from the multiple sensors. The anomaly detection system operates on a computing device including at least one processor, and instructions that cause the processor to receive sensor data from the multiple sensors, utilize advanced machine learning model techniques to detect both static and dynamic anomalies in an agricultural field surrounding the agricultural vehicle, and control operations of the agricultural vehicle based on the detected anomalies. Related agricultural vehicles and methods are also disclosed.