This collaborative effort between Protostar Labs and the University of Zagreb Faculty of Electrical Engineering and Computing (FER) has the goal to revolutionize multispectral imaging in industrial applications. Protostar Labs, a leading innovator in AI and computer vision, brings its expertise in developing cutting-edge algorithms and software solutions. FER, renowned for its research in electrical engineering and computing, contributes its deep understanding of sensor technology, optics and image processing. This partnership combines the strengths of both organizations to create a truly innovative multispectral imaging system.
The Need for Affordable Multispectral Imaging
Traditional multispectral cameras, while offering powerful capabilities for capturing and analyzing images across different wavelengths of light, often come with a hefty price tag. These systems can cost upwards of €25,000, making them inaccessible to many businesses and research institutions. This high cost significantly limits the widespread adoption of multispectral imaging across various sectors. This project addresses this challenge head-on by developing a system that combines affordability with cutting-edge technology, democratizing multispectral analysis and making it accessible to a broader range of industries. The significant cost difference between this project and traditional multispectral cameras is a key factor driving its potential to revolutionize the field.
Technological Foundation
At its core, Antares multispectral imaging system leverages the accessibility and high resolution of readily available RGB cameras, enhancing them with specialized optical filters. The custom filter design enables capture of specific wavelengths of light from ultra-violet, visible and to the near-infrared spectrum, allowing the camera to “see” what would otherwise be invisible to the naked eye. This capability enables the detection and analysis of materials based on their unique spectral signatures, which can be thought of as “fingerprints” in the electromagnetic spectrum. By analyzing these captured wavelengths, the project can identify and differentiate materials based on their unique spectral responses.
The Antares hardware setup is further enhanced by integrating advanced computer vision and AI algorithms. This integration allows the system to automatically detect and analyze materials in real-time, streamlining the process and reducing the need for manual analysis. This automation is crucial for industrial applications where speed and efficiency are paramount.
This sophisticated combination enables the Antares system to perform several key functions:
- Automatic detection and analysis of materials: By capturing images in multiple wavelengths, the system can identify and analyze different materials based on their unique spectral signatures. This capability has significant implications for various applications, from quality control in manufacturing to environmental monitoring.
- Enhanced accuracy: AI algorithms play a crucial role in improving the accuracy of object classification and recognition. Deep learning models, trained on vast datasets, can discern subtle differences in spectral data, leading to more precise and reliable results.
- Real-time decision-making: The system is designed to analyze data and provide insights in real-time. This capability allows for immediate action in time-sensitive situations, such as identifying contaminants in a food production line or detecting anomalies in a manufacturing process.
- High spatial resolution multispectral imaging: The core of low cost foundation of using readily available RGB sensors with specialized optical filter design enables high spatial resolution area-scan imaging, compared to other hyperspectral solutions, while enabling applications where push-broom or line-scan cameras are not suitable.
Potential Impact and Future Directions
This multispectral imaging project holds immense potential to revolutionize various industrial sectors by providing an affordable and accessible solution for multispectral analysis. The successful implementation of this technology could lead to significant advancements in several areas:
- Contamination detection in food production: Ensuring food safety and quality is paramount. The system can be used to identify contaminants in food products that are invisible to the naked eye. By analyzing spectral data, the system can detect the presence of foreign objects, bacteria, or other contaminants, enabling prompt corrective actions and preventing potential health hazards.
- Improved quality control in manufacturing: This project can enhance quality control processes by enabling the detection of microscopic defects and anomalies in manufactured products. For instance, the system could be used to identify microscopic cracks in circuit boards, ensuring the reliability of electronic devices. It could also be used to detect counterfeit products by analyzing their spectral characteristics, helping to protect consumers and businesses from fraudulent goods.
- Increased efficiency in agriculture: By providing detailed information about soil conditions and crop health, this project can contribute to increased efficiency in agriculture. Farmers can use this information to optimize irrigation schedules, tailor fertilizer applications to specific needs, and identify early signs of disease or stress in plants. This precision agriculture approach can lead to improved crop yields, reduced resource consumption, and minimized environmental impact.
- Advancements in environmental monitoring: Monitoring the health of our environment is crucial for sustainable development. This project can be deployed for various environmental monitoring applications, such as detecting pollution in water bodies, assessing the impact of deforestation, and monitoring changes in ecosystems. The system’s ability to capture and analyze spectral data can provide valuable insights into environmental changes, enabling timely interventions and informed decision-making.
Advantages
This project offers several advantages over traditional multispectral cameras:
| Feature | Antares Multispectral Imaging | Traditional Multispectral Cameras |
| Cost-Effectiveness | Utilizes affordable, readily available RGB cameras with specialized filters, significantly reducing the cost. | Can cost over 25,000 euros, making them less accessible. |
| Scalability | Designed to be scalable, allowing for the integration of multiple cameras and filters to cover larger areas or analyze multiple spectral bands simultaneously. | Scalability can be limited due to the high cost and complexity of individual cameras. |
| Spatial resolution | Combining high resolution RGB sensors with specialized filters | Lower resolution images after demosaic and wavelength extraction |
The cost-effectiveness of this project is particularly noteworthy. By utilizing RGB cameras, the project significantly reduces the cost of multispectral imaging, making it a more accessible solution for various industries. This lower cost can democratize access to multispectral imaging, potentially revolutionizing various sectors by enabling smaller businesses and research institutions to utilize this powerful technology.
Conclusion
The multispectral imaging project represents a significant leap forward in multispectral imaging technology. By combining affordability, flexibility, and advanced AI algorithms, the project has the potential to transform various industries, from manufacturing and agriculture to environmental monitoring. The project’s emphasis on cost-effectiveness makes multispectral analysis accessible to a wider range of users, democratizing this powerful technology and paving the way for new applications and discoveries. As the project progresses and matures, we can expect to see further advancements and exciting developments in this field, unlocking new possibilities and driving innovation across various sectors.
Find all the information on the Antares Multispectral Imaging here: https://protostar.ai/antares/
Official joint project information can be found here: website
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Funding
The Project is financed by the Recovery and Resilience Facility in the framework of the National Recovery and Resilience Plan 2021-2026. of the European Union – NextGenerationEU.
