Agricultural Machine Anomaly Detection for Unforeseen Obstacles
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Solution Overview
Problem
Existing agricultural machine monitoring systems struggle to detect unforeseen obstacles in unstructured environments due to the need for extensive training on predefined object classes, leading to potential collisions and reduced operator trust in operator assistance systems and increased workload.
Innovation Solution
A control system that utilizes an autoencoder architecture to encode and decode image data, generating an anomaly map by comparing reconstructed images with input images, allowing for the detection of any anomalies in the environment without requiring extensive training on multiple object classes, and can control operational components such as steering and propulsion systems to avoid detected obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detection algorithms are trained on predefined object classes, then detection accuracy for common objects is improved, but the system fails to detect unforeseen objects in unstructured environments
Solution Approach 1:
Instead of training the system to detect specific objects (positive approach), the patent inverts the approach by training the system to recognize normal environments and detecting anything that deviates from this normal state as an anomaly. This allows detection of unforeseen objects without requiring predefined object classes.
Solution Approach 2:
The system uses the environment data itself to define what is normal, without requiring external training data for each object class. The system serves itself by learning from the operational environment and automatically adapting to detect anomalies.
2Adaptability or versatility
If extensive training data for multiple object classes is collected, then detection coverage is improved, but computational power and training time requirements increase significantly
Solution Approach 1:
The patent extracts only the essential feature of detecting anomalies rather than detecting every possible object class. By taking out the need for extensive object-specific training, the system achieves good detection coverage with significantly reduced computational requirements.
3Device complexity
If predefined object classes are used for detection, then system complexity is reduced, but the workload for operators increases and operator trust decreases
Solution Approach 1:
The anomaly detection system serves multiple detection purposes universally without requiring separate detection mechanisms for each object type. This multi-functional approach reduces operator workload as a single system handles diverse detection needs.
Data Source
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AI summary
Methods and systems are provided for monitoring operation of an agricultural machine. Image data indicative of an input image of a working environment of the agricultural machine is receive and encoded utilising an encoder network to map the image data to a lower-dimensional feature space. The encoded data is then decoded form a reconstructed image of the working environment which is compared with the input image to identify anomalies within the working environment. One or more operable components associated with the machine may be controlled based on the identification of one or more anomalies.