Agricultural Vehicle Obstacle Detection for Roadside Mowing Hazards
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Solution Overview
Problem
Challenges exist in roadside mowing operations due to hidden obstacles like telecom and power boxes, which pose risks to mowing equipment, infrastructure, and safety hazards, leading to costly repairs and operational inefficiencies.
Innovation Solution
A guidance system for agricultural vehicles equipped with image sensors and machine learning algorithms to identify and classify objects, generating alarms for potential hazards, and adjusting vehicle operations to avoid damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mowing operations are conducted without detection systems, then operational speed and simplicity are maintained, but equipment damage risk and repair costs increase due to hidden obstacles
Solution Approach 1:
The system performs preliminary detection of obstacles using image sensors and machine learning classification before the mowing operation proceeds. By identifying and classifying objects in advance, the system allows operators to take preventive actions, avoiding equipment damage before it occurs.
Solution Approach 2:
The patent introduces an intermediary detection system consisting of image sensors and machine learning algorithms that mediate between the mowing equipment and hidden obstacles. This intermediary layer processes visual data and provides classification information, enabling informed decision-making without direct contact between the mower and potential hazards.
2Measurement precision
If multiple image sensors and machine learning models are deployed to improve object detection accuracy, then detection precision increases, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the detection task by using multiple specialized image sensors (e.g., RGB, thermal, SWIR) that capture different spectral information, and employs machine learning models that separately process and classify different object types. This segmentation allows each component to be optimized for its specific function while working together to achieve comprehensive detection.
Solution Approach 2:
The machine learning classification system is designed to universally identify and classify multiple types of objects (telecom boxes, power boxes, rocks, debris) using a unified framework. This multi-functional approach allows the same system architecture to handle diverse detection tasks, reducing overall system complexity compared to having separate specialized systems for each object type.
3Reliability
If real-time object detection and classification systems are implemented, then safety and equipment protection improve, but operational time and processing requirements increase
Solution Approach 1:
The system implements periodic scanning and detection cycles rather than continuous full-analysis mode. Image sensors capture frames at regular intervals, and the machine learning models process these periodic inputs to identify and classify objects. This periodic approach maintains safety while reducing computational burden compared to continuous real-time analysis.
Solution Approach 2:
The system performs preliminary object identification and classification in advance during the detection phase, before the mowing operation reaches critical decision points. By preparing classification results beforehand, the system minimizes processing delays during actual mowing operations, maintaining both safety and operational efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances safety and efficiency by detecting and avoiding obstacles, reducing equipment damage and repair costs, and improving visibility along roadsides.
Implementation Method 1
The image sensor may include at least one of a thermal camera, a light detection and ranging (LIDAR) camera
Implementation Method 2
The image sensor may include at least one of a thermal camera
Implementation Method 3
The image sensor may include at least one of a thermal camera, a light detection and ranging (LIDAR) camera, a short wave infrared (SWIR) camera, a near infrared camera (NIR)
Implementation Method 4
The image sensor may include at least one of a thermal camera, a light detection and ranging (LIDAR) camera, a short wave infrared (SWIR) camera, a near infrared camera (NIR), an RGB camera, or a polarized camera
Implementation Method 5
Analyzing the image data may include utilizing one or more machine learning models to identify and classify one or more objects depicted within the image data
Implementation Method 6
Causing the alarm to be output via an input/output device of the guidance system may include causing an audible alarm to be output
Implementation Method 7
Causing the alarm to be output via the input/output device of the guidance system may include causing a visual alarm to be output on a display
Data Source
Figure 1
Figure 2A~2B
Figure 3A~3B
AI summary
A guidance system for controlling operation of an agricultural vehicle includes at least one processor and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the guidance system, during an agricultural operation, to receive image data from an image sensor, analyze the image data to identify and classify one or more objects depicted within the image data, responsive to identifying one or more objects of interest, generate an alarm and cause the alarm to be output via an input/output device of the guidance system.