Radar-Camera Object Recognition System for Large Machines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing object detection and recognition systems for large machines face challenges in processing vast amounts of data from multiple cameras in real-time, while radar-based systems lack specificity, necessitating a solution that balances speed and accuracy for collision avoidance and object classification.
Innovation Solution
An object recognition system that combines radar and camera data, using a processor to detect objects with radar, map radar data to corresponding image data, and classify objects using image processing techniques, while limiting unnecessary image data processing and employing an image transformer to standardize object orientation for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object detection and recognition systems use multiple cameras to cover all sides of large machines, then object detection coverage is improved, but computational load and processing time increase significantly
Solution Approach 1:
The system segments the processing task by first using radar to detect objects and obtain their spatial information, then using this information to selectively process only relevant image data from multiple cameras. This divides the originally monolithic image processing task into a two-stage process: radar-based object detection followed by targeted image processing only for detected objects, significantly reducing overall computational load while maintaining comprehensive detection coverage.
Solution Approach 2:
The system performs preliminary object detection using radar data before processing image data. By first identifying potential objects of interest through radar scanning and obtaining their spatial coordinates, the system prepares a list of targets that require further image-based analysis. This preliminary action prevents the system from processing all image data uniformly, instead focusing computational resources only on regions containing detected objects.
2Productivity
If radar-based object detection systems are used for large machines, then processing speed is improved, but object classification accuracy deteriorates due to lack of specificity
Solution Approach 1:
The system merges radar detection capabilities with camera-based image processing to create a hybrid object recognition system. Radar provides fast object detection and spatial positioning, while cameras provide detailed visual information for accurate classification. The two data sources are integrated through coordinate transformation, where radar-detected objects are mapped to corresponding image regions for detailed analysis, combining the speed advantages of radar with the accuracy advantages of visual processing.
Solution Approach 2:
The system uses spatial coordinate information as an intermediary to connect radar data and image data. The radar detection results, expressed in radar coordinate system, are transformed and mapped to the camera image coordinate system. This intermediary mapping process enables the system to efficiently locate and extract image regions corresponding to radar-detected objects, facilitating accurate classification without requiring full image processing.
3Reliability
If object detection systems classify objects according to type (equipment, large vehicle, person), then safety protocols can be initiated based on classification, but additional processing and computing resources are required
Solution Approach 1:
The system applies local quality processing by performing detailed image-based classification only on specific regions of interest identified by radar, rather than processing the entire image uniformly. By mapping radar-detected objects to their corresponding locations in the image and processing only those localized regions, the system achieves accurate object type classification (person, vehicle, equipment) while minimizing overall computational complexity.
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
This system enables efficient and accurate object detection and classification on large machines, reducing computational load and improving safety by processing only relevant image data and transforming images to facilitate consistent orientation recognition, thus enhancing collision avoidance and operator safety.
Implementation Method 1
a radar device configured to generate radar data of the environment of the machine
Data Source
AI summary
A object recognition system has a camera configured to generate image data of an environment of a machine and a radar device configured to generate radar data of the environment of the machine. The object recognition system also has a processor configured to detect an object in the environment based on the radar data, map the radar data to a portion of the image data corresponding to the detected object, and classify the detected object using the mapped portion of the image data.