Simplify camera integration for Industrial Applications

Cameras have become indispensable in modern industrial applications. In many cases, intelligent vision systems support and even enable specific applications. They detect and record obstacles, recognize objects, and map them. They can be found in cars, intralogistics, autonomous mobile robots, and drones. Advances in technology have made vision system components smaller, more affordable, and accessible for a wide range of uses.

Equipping an application with vision capabilities can be extremely complex, but in many cases it is now easier than you might think. There are a few key things to keep in mind. In our article, we explain what you need to know to get started with your application.

Embedded Vision Applications

Embedded vision applications are rapidly expanding across a diverse range of industries, each leveraging the technology to address unique challenges and unlock new possibilities. In manufacturing, embedded vision systems play a crucial role in quality control, assembly line automation, and inspection, providing detailed analysis and insights that drive efficiency and accuracy.

The healthcare sector benefits from embedded vision through advanced medical imaging, patient monitoring, and diagnostic tools, all of which contribute to improved patient care and outcomes. In transportation, embedded vision is at the heart of autonomous vehicles, enabling them to perceive their surroundings and make informed decisions on the move. As embedded vision systems continue to evolve, their ability to deliver actionable insights and enhance system performance will only grow, paving the way for even more innovative uses in the future.

Challenges in camera integration

To give a machine vision capabilities, you need a good understanding of the application you are building. The two key questions here are: What result do you want to achieve and what factors play a role? Creating high-resolution images for aerial mapping requires different capabilities and characteristics from a camera system than measuring fill levels in a dark factory hall. Thermal and mechanical influences must be taken into account and the image results required must be clearly defined. While some applications can manage with a relatively low resolution of 2 to 3 megapixels, applications in quality control, such as surface inspection of wafers, require the “last few percent” of performance from a camera system. This is the application side of the requirements profile for camera integration.

The hardware side, on the other hand, looks like this: A camera system consists of a few components. First, you need an image sensor, a suitable lens, control boards, cabling, and appropriate drivers or software. Image sensors are usually integrated directly onto a circuit board and offered for industrial purposes. This is called a camera module. The control logic for the sensor is located on the circuit board. Energy-efficient controllers such as an ESP32 are often used.

These can be flashed with the appropriate code. For demanding industrial applications, however, there are also powerful system-on-modules (SOM) such as those from NVIDIA and AMD (formerly Xilinx) with system-on-chips (SOC) developed for specific applications, such as those from NXP, which optimally integrate powerful image sensors such as those from Sony. There is a wide range of SOMs, SOCs, adapter boards, cabling options, and software stacks available. This is urgently needed because the choice is as wide as the range of applications.

In many cases, the camera system functions as a device that integrates both image capture and image processing within a compact unit, enabling miniaturization and application-specific customization. An embedded vision system combines hardware and software to capture, process, and analyze images within a single device, offering flexibility and transformative potential for a wide range of applications.

When discussing the control logic and SOMs, it is important to note that FPGA design plays a key role in achieving low latency and supporting advanced applications like image processing and deep learning. There is a wide range of SOMs, SOCs, adapter boards, cabling options, and software stacks available, which is urgently needed because the choice is as wide as the range of applications. Image processing is essential in interpreting visual data for real-time applications, enabling intelligent and responsive embedded vision systems.

Planning camera integration to control environmental influences

Let’s assume that a camera is to be integrated into a car and used as a rear view camera: When planning camera integration, it is important to set clear objectives to ensure all requirements are met and the system aligns with the intended goals. The camera system must function with consistently good performance both day and night, withstand relatively high temperatures and vibrations. In addition, the camera should deliver high-resolution image data in real time, which is processed by an electronic control unit (ECU) (for example, for obstacle detection) and then forwarded to a domain controller or the vehicle computer.

This requires an image sensor with a high dynamic range to adequately capture large differences in brightness, as well as a powerful SOM that is ideally designed for automotive applications and, optimally, pre-certified. The camera system must be correctly focused, and thermal influences must not distort the image result. Shock and vibration influences must also be taken into account. If there is also a long distance between the ECU or vehicle computer and the camera in the car, the appropriate cabling must be available and, in the case of high data volumes, not only must the SOM mentioned above be able to keep up, but a suitable, powerful data interface must also be available.

During system validation and performance checks, it is essential to take action to verify system security and review all relevant protocols before deployment.

Comprehensive modular system for perfect integration

Nowadays, there are suitable components available to overcome all these hurdles. FRAMOS offers a complete, optimally coordinated ecosystem that is ideally suited to every conceivable application.

As a rule, a kit for this modular system, known as FSM:Ecosystem, consists of the following components:

• FRAMOS Sensor Module (FSM): Soldered image sensor and optics
• FRAMOS Sensor Adapter (FSA): Adapter board for the data interface
• FRAMOS Functional Adapter (FFA): Adapter board for connection or cabling
• FRAMOS Processor Adapter (FPA): Adapter board for the target platform
• The image sensor modules integrated into this ecosystem are designed for compatibility with a wide range of processors and are therefore ideal for edge AI and computer vision applications in various industries.

Embedded vision cameras within this ecosystem are designed for compatibility with a wide range of processors, making them ideal for edge AI and computer vision applications across diverse industries.

In addition, there are suitable lenses, drivers, and reference applications for easy entry into development. A conceivable kit could thus consist of a high-resolution image sensor or image sensor module with an FSA that supports the SVLS-EC data interface, which is designed for high data transfer capacities. To bridge the possible distance of 7 meters in a car, FFAs that support GMSL via coaxial cables would be used, allowing cable lengths of up to 15 meters, as well as an FPA as an intermediary to a suitable high-performance SOM such as those from AMD’s UltraScale series (or Xilinx).

In fact, the flexibility and modularity of this system empower developers to quickly adapt to new requirements and unlock the transformative potential of embedded vision technology.

Integrating is easy – thanks to FRAMOS’ capabilities

In addition to the right hardware, a range of skills are also required to achieve optimal image results. To stay with the car example, thermal influences must be minimized and lenses must be mounted in such a way that they are permanently shock and vibration resistant. In addition, lens focusing is crucial for sufficiently good image resolution. For optimal image results, the image signal processor (ISP) must also be tuned and the camera system calibrated and tested.

During ISP tuning, camera systems are tested under different lighting conditions and optimally configured for these conditions. These services are usually very complex, which is why they are usually performed by imaging experts or imaging laboratories. To give a striking example: the virtually tilt-free integration of the optics is a highly sensitive process. Even slight deviations lead to suboptimal image results.

The problem becomes more acute the larger the image sensor is. However, some applications require the maximum performance of a camera system. This process of setting the optics is then performed by a machine. This is what is meant by active alignment. However, this is not always necessary. For some applications, results that do not require extreme resolution are sufficient, for example when measuring fill levels in bottles.

Advanced integration capabilities help ensure reliable performance and product differentiation.

Learning by doing: Engineers integrate the camera themselves

The easiest way to familiarize yourself with camera integration is to order a dev kit to test the capabilities of an image sensor or camera system. This allows you to quickly develop prototypes. Once a suitable prototype system has been developed, FRAMOS can help optimize it and bring it into series production. FRAMOS offers smart solutions, especially for small and medium-sized companies, to achieve high quality standards that would otherwise only be achievable for extremely large series production. One lever is FSM:GO.

FRAMOS has developed this series of optical sensor modules for rapid prototype development. It has a manageable selection of suitable lenses, and FSM:GO is designed in such a way that certain steps in the integration process are eliminated. For example, FSM:GO can be used to significantly reduce lens focusing time. The selection of FSM:GO modules provides a reference framework in which certain applications function optimally.

Best Practices for Embedded Vision

Implementing embedded vision systems successfully requires careful planning and adherence to best practices. Engineers should begin by selecting hardware and software components—such as cameras, processors, and algorithms—that are specifically designed to meet the application’s requirements for power, resolution, and compliance.

Collaboration is crucial; working closely with industry experts and researchers ensures that the system is tailored to the end-user’s needs and leverages the latest advancements in embedded vision technology. It’s also important to regularly verify system performance through rigorous testing and evaluation, making adjustments as necessary to maintain optimal functionality. By focusing on these best practices, companies can develop embedded vision systems that not only meet but exceed their operational needs, providing a clear advantage in today’s fast-paced, technology-driven markets.

Conclusion

For easy camera integration into an embedded system, you need to know exactly what image results are required and in what kind of environment the camera system will be used. Suitable components for building and integrating the camera are available for virtually every application.

The easiest and fastest way to integrate a high-performance camera is with a ready-made system such as the optical sensor modules of the FSM:GO series. For even more precise adaptation, suitable components are also available.