Millimeter-Wave Radar and Camera Fusion for Reliable Object Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing object detection systems face inaccuracies due to reliance on cameras alone, which are hindered by factors like excessive or overlapping objects and insufficient lighting, while radar devices provide accurate location but lack object identification capabilities.
Innovation Solution
Combining millimeter-wave radar with cameras and computer-vision applications to fuse object coordinates and implement a reliability score, ensuring accuracy by comparing sensor and camera counts against a threshold, and involving human verification when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If camera-based computer vision methods are used for object detection, then object identification capability is provided, but detection accuracy deteriorates due to excessive objects, overlapping objects, and insufficient lighting
Solution Approach 1:
The patent combines camera-based computer vision methods with radar-based detection systems to create a hybrid detection system. The camera provides object identification while the radar provides accurate location detection regardless of lighting conditions or object density. By merging these two systems, the patent achieves both object identification capability and high detection accuracy simultaneously, resolving the technical contradiction between information loss and measurement precision.
2Measurement precision
If radar devices are used for object detection, then detection accuracy is improved, but object identification capability is lost
Solution Approach 1:
The patent merges radar detection with camera-based computer vision to create a complementary system. The radar component ensures high detection accuracy for object location and movement, while the camera component restores object identification capability. The system fuses data from both sources, allowing the radar to handle detection in challenging conditions while the camera provides visual identification, thus resolving the contradiction between measurement precision and information loss.
3Device complexity
If exclusive camera-based systems are used, then device complexity is reduced, but reliability deteriorates in challenging environmental conditions
Solution Approach 1:
The patent combines camera and radar systems into an integrated detection platform. While this increases device complexity compared to exclusive camera systems, it significantly improves reliability by providing complementary detection capabilities. The radar ensures reliable detection in low-light or crowded conditions where cameras fail, while the camera provides reliable identification in good conditions. The system includes fusion algorithms that coordinate both sensors, making the complexity management worthwhile for the achieved reliability improvement.
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 detection accuracy and reliability by correcting inaccuracies through sensor fusion and human oversight, suitable for applications like traffic supervision, surveillance, and security inspections.
Implementation Method 1
radar devices can detect objects and their location with greater accuracy than a camera
Data Source
AI summary
Systems, methods, apparatuses, and computer program products for detecting, identifying, and monitoring objects. One method may include detecting, by a camera, at least one object according to at least one coordinate; detecting, by a sensor, at least one object according to at least one coordinate; fusing, by a computer vision application, the at least one coordinate of the at least one object detected by the camera with the at least one coordinate of the same at least one object detected by the sensor; determining, by a computer vision application, whether the number of objects detected by the camera equals the number of objects detected by the sensor; and determining, by the computer vision application, whether a score associated with the one or more additional functions is below a predetermined threshold.