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Smart camera
Smart camera
Smart camera
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Early smart camera (ca. 1985, in red) with an 8MHz Z80 compared to a modern device featuring Texas Instruments' C64 @1GHz

A smart camera is a machine vision system which, in addition to image capture circuitry, is capable of extracting application-specific information from the captured images, along with generating event descriptions or making decisions that are used in an intelligent and automated system.[1][2] A smart camera is a self-contained, standalone vision system with built-in image sensor in the housing of an industrial video camera. It is also known as an intelligent camera, a (smart) vision sensor, an intelligent vision sensor, a smart optical sensor, an intelligent optical sensor, a smart visual sensor, or an intelligent visual sensor.

The vision system and the image sensor can be integrated into one single piece of hardware known as intelligent image sensor or smart image sensor. It contains all necessary communication interfaces, e.g. Ethernet, as well as industry-proof 24V I/O lines for connection to a PLC, actuators, relays or pneumatic valves, and can be either static or mobile.[3] It is not necessarily larger than an industrial or surveillance camera. A capability in machine vision generally means a degree of development such that these capabilities are ready for use on individual applications. This architecture has the advantage of a more compact volume compared to PC-based vision systems and often achieves lower cost, at the expense of a somewhat simpler (or omitted) user interface. Smart cameras are also referred to by the more general term smart sensors.[4]

History

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The first publication of the term smart camera was in 1975[5] as according to Belbachir et al.[6] In 1976, the General Electric's Electronic Systems Division indicated requirements of two industrial firms for smart cameras in a report for National Technical Information Service.[7] Authors affiliated in HRL Laboratories defined a smart camera as "a camera that could process its pictures before recording them" in 1976.[8] One of the first mentions of smart optical sensors appeared in a concept evaluation for satellites by NASA and General Electric Space Division from 1977.[9] They were suggested as a means for intelligent on-board editing and reduction of data.

Smart cameras have been marketed since the mid 80s. In the 21st century they have reached widespread use, since technology allowed their size to be reduced and their processing power reached several thousand MIPS (devices with 1 GHz processors and up to 8000MIPS are available as of end of 2006).

Artificial intelligence and photonics boost each other.[10] Photonics accelerates the process of data collection for AI and AI improves the spectrum of applications of photonics. In 2020, Sony has launched the first intelligent vision sensors with AI edge computing capabilies.[11] It is a further development of Exmor technology.

Components

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A smart camera usually consists of several (but not necessarily all) of the following components:

  • Image sensor (matrix or linear, CCD- or CMOS)
  • Image digitization circuitry
  • Image memory
  • processor (often a DSP or suitably powerful processor)
  • program- and data memory (RAM, nonvolatile FLASH)
  • Communication interface (RS-232, Ethernet)
  • I/O lines (often opto-isolated)
  • Lens holder or built in lens (usually C, CS or M-mount)
  • Built in illumination device (usually LED)
  • Purpose developed real-time operating system (For example VCRT)
  • Optional video output (e.g. VGA or SVGA)
  • Energy supply by e.g. energy harvesting

Fields of application

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Having a dedicated processor in each unit, smart cameras are especially suited for applications where several cameras must operate independently and often asynchronously, or when distributed vision is required (multiple inspection or surveillance points along a production line or within an assembly machine). In general smart cameras can be used for the same kind of applications where more complex vision systems are used, and can additionally be applied in some applications where volume, pricing or reliability constraints forbid use of bulkier devices and PC's.

Typical fields of application are:

Developers can purchase smart cameras and develop their own programs for special, custom made applications, or they can purchase ready made application software from the camera manufacturer or from third party sources. Custom programs can be developed by programming in various languages (typically C or C++) or by using more intuitive, albeit somewhat less flexible, visual development tools where existing functionalities (often called tool or blocks) can be connected in a list (a sequence or a bi-dimensional flowchart) that describes the desired flow of operations without any need to write program code. The main advantage of the visual approach versus programming is the shorter and somewhat easier development process, available also to non-programmers. Other development tools are available with relatively few but comparatively high level functionalities, which can be configured and deployed with very limited effort.

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Smart camera

View on Grokipedia
from Grokipedia
A smart camera is a compact, integrated vision system that combines an , onboard processing hardware, and software algorithms to capture, analyze, and interpret visual data in real time, often delivering decisions or alerts rather than raw footage. Unlike conventional cameras, which primarily transmit unprocessed images to external systems, smart cameras perform localized computation to enable efficient, autonomous operation in resource-constrained environments. Key components of a smart camera include a high-resolution —typically or CCD for capturing light into digital signals—a dedicated processor such as a digital signal processor (DSP), field-programmable gate array (FPGA), or system-on-chip (SoC) for executing image processing tasks, and integrated interfaces for input/output and communication. and illumination systems are often modular, allowing customization, while power-efficient designs support wireless connectivity via protocols like or , making them suitable for distributed networks. These elements are housed in a rugged enclosure, sometimes achieving IP67 ratings for durability in industrial settings, with processing speeds capable of handling tasks like reading at over 25 items per second. Smart cameras have evolved significantly since the early 2000s, driven by advances in embedded computing, , and sensor miniaturization, transitioning from centralized systems to decentralized, intelligent nodes in camera networks. They are widely applied in industrial automation for quality inspection and defect detection, security systems for real-time surveillance and intrusion alerts, healthcare for monitoring, and intelligent transportation for and license plate recognition. Recent developments, particularly since 2023, integrate and directly at the sensor level to improve accuracy in complex tasks like and object , enhancing their role in IoT ecosystems.

Overview

Definition and Characteristics

A smart camera is a self-contained imaging device that integrates an , onboard processing capabilities, and often communication interfaces to capture, analyze, and interpret visual data autonomously, enabling tasks such as without reliance on external resources. Unlike traditional systems, its primary output is not raw imagery but derived , such as scene descriptions or decisions, processed directly at . This design evolved from basic digital cameras by incorporating embedded to handle complex visual analysis in real time. Key characteristics of smart cameras include their compact form factor, which allows integration into diverse environments, and the use of edge computing for low-latency, processing directly on the device. They typically incorporate (AI) and (ML) algorithms to enable autonomous decision-making, such as classifying detected elements in the visual field. Additionally, support for communication protocols like Ethernet for industrial settings or for consumer applications facilitates data transmission to networks or other devices, enhancing in systems like or . Core capabilities of smart cameras encompass to identify unusual events in monitored areas, for recognizing recurring visual motifs, and automated alerts triggered by processed data, such as notifying users of potential security breaches. These features enable applications ranging from industrial to , where the camera operates independently to reduce bandwidth demands on connected systems. Smart cameras are distinguished by type, with embedded variants designed for fixed installations within larger systems, such as manufacturing lines, where they prioritize seamless integration and minimal footprint. In contrast, standalone units offer portability for flexible deployment in scenarios like mobile surveillance, functioning independently with built-in power and storage options.

Differences from Traditional Cameras

Smart cameras differ fundamentally from traditional cameras in their core functionality and output. Traditional cameras primarily capture and transmit raw image data to an external device, such as a or server, for subsequent processing and analysis. In contrast, smart cameras integrate onboard processing capabilities, enabling them to analyze captured images directly and produce actionable insights, such as object classifications, measurements, or defect detections, without relying on external hardware. This on-device allows smart cameras to operate as standalone systems, reducing the need for data transfer and minimizing dependency on separate resources. Architecturally, smart cameras consolidate the , processor, and software into a single, compact unit, which contrasts with the multi-component setup of traditional systems that often include a camera, frame grabber, cabling, and a host PC. This integrated design in smart cameras lowers latency by eliminating the delays associated with transmitting large volumes of raw over interfaces like USB or GigE to an external processor. For instance, traditional setups may experience higher latency due to buffering and network overhead, whereas smart cameras can achieve real-time processing speeds, such as up to 77 frames per second in FPGA-based architectures optimized for tasks. These differences yield significant performance advantages for smart cameras, particularly in demanding environments. By requiring fewer components, smart cameras reduce cabling complexity, power consumption (e.g., as low as 368 mW in hardware-accelerated designs), and to interference, making them ideal for standalone deployment in industrial or harsh settings where external PCs might fail. Traditional systems, while more flexible for complex, multi-camera applications, demand greater infrastructure and are less efficient for scenarios. In practical use cases, a traditional camera might stream video footage to a central server for human or software review, as in basic or quality checks. A smart camera, however, can autonomously flag defects in real-time during inspections, such as detecting anomalies on assembly lines without human intervention, thereby enhancing efficiency and reducing response times.

History

Early Developments

The origins of smart cameras trace back to the early (CCTV) systems developed during , which served as foundational precursors by enabling remote video monitoring without broadcast capabilities. In 1942, German engineer Walter Bruch designed the first practical CCTV system to observe launches from a safe distance, using a single camera connected to monitors via for real-time surveillance. These military applications in the laid the groundwork for integrating imaging with basic , though early systems lacked onboard computation and relied on analog transmission. Advancements in the shifted toward technologies that enabled future on-board processing in cameras. The invention of the (CCD) sensor in 1969 by and at revolutionized image capture by converting light into digital signals, allowing for electronic manipulation rather than purely analog handling. By the mid-, prototypes like Kodak's first demonstrated the feasibility of digital sensors, which provided the electrical interface necessary for integrating computational elements directly with imaging hardware. These developments in digital sensors overcame limitations of film-based and analog systems, setting the stage for embedded processing in and contexts. The 1980s marked the emergence of the first smart camera prototypes, primarily from university research labs, which integrated basic processors with image sensors for simple analysis tasks. These early systems combined CCD or emerging sensors with limited CPU or processors (DSPs) to perform low-level operations like edge detection in applications, reducing the need for external computing resources. For instance, prototypes developed at institutions such as those referenced in early embedded vision studies incorporated rudimentary on-chip processing for real-time feature extraction, evolving from standalone CCTV to self-contained units. This period saw the transition from passive to active interpretation, with university-led innovations focusing on compact integration for industrial and . Early smart cameras faced significant challenges that confined their use to settings and initial industrial trials. High development costs, driven by custom fabrication of integrated circuits, made widespread adoption impractical, while bulky designs—often requiring separate power supplies and cooling for processors—limited portability. Additionally, the constrained processing power of 1980s-era microprocessors restricted capabilities to basic algorithms, such as threshold-based , without support for complex analysis. These hurdles ensured that smart cameras remained experimental tools, primarily tested in controlled environments like academic prototypes for tasks.

Modern Advancements

The marked a pivotal era for the commercialization of smart cameras, particularly in industrial settings, where embedded processors enabled compact, standalone systems for tasks like quality inspection and . These processors, initially low-end but increasingly capable, allowed smart cameras to perform on-board image processing without reliance on external hosts, reducing system complexity and costs for applications in and assembly verification. A key enabler was the rise of complementary metal-oxide- () sensors, which leveraged existing fabrication processes to achieve significant cost reductions—by the mid-, captured approximately 70% of the market share compared to (CCD) alternatives. This affordability facilitated broader adoption in embedded vision systems, transitioning smart cameras from experimental prototypes to viable commercial products. Entering the 2010s, smart cameras advanced through the integration of (AI) and (ML), enhancing capabilities for advanced recognition tasks such as , facial identification, and scene analysis. Breakthroughs in deep neural networks, particularly following GPU-accelerated image classification advancements in , enabled more efficient on-device processing for these functions, surpassing traditional algorithmic approaches in accuracy and speed. By the mid-2010s, this integration had driven widespread adoption in , where smart cameras became standard for high-volume applications like reading, , and robotic guidance in industrial environments. Concurrently, smart cameras gained prominence in automotive advanced driver-assistance systems (ADAS), with camera-based technologies standardizing features like automatic emergency braking (AEB) and , thereby improving vehicle safety metrics. In the 2020s, edge AI has further transformed smart cameras by supporting real-time inference directly on-device, minimizing latency and eliminating cloud dependency for privacy-sensitive or bandwidth-constrained scenarios. Processors like modules, offering up to 275 tera operations per second (TOPS), facilitate instant analytics such as motion tracking and in fields like and healthcare monitoring. By 2025, trends emphasize connectivity integration, which accelerates data transmission in networked systems, enabling seamless 4K video streaming and collaborative processing across distributed camera arrays in smart cities and industrial IoT setups. These developments have shifted smart cameras from niche tools to mainstream enablers of automation, exemplified by their role in ADAS proliferation, where adoption rates in new vehicles exceeded 50% for camera-dependent features by the late 2010s.

Technical Components

Hardware Elements

Smart cameras rely on integrated hardware components that enable on-device image capture and preliminary , distinguishing them from passive devices. At the core is the , which converts light into digital signals; common types include (CCD) sensors, known for low noise and high uniformity, and complementary metal-oxide-semiconductor (CMOS) sensors, which offer random pixel access, higher frame rates, and larger dynamic range, facilitating compact designs. Lenses and systems focus incoming light onto the sensor, ensuring sharp across various focal lengths and environmental conditions. Integrated processors, such as central units (CPUs) or processing units (GPUs), handle computational tasks directly on the camera; modern examples include system-on-chip (SoC) platforms like the series, which incorporate neural units (NPUs) for efficient . Additional hardware elements support sensor operation and . Lighting modules, such as or LED illuminators, provide controlled illumination for low-light scenarios, enhancing capture reliability in industrial settings. Memory components, including (RAM) for buffering frames and flash storage for and , enable temporary storage during processing. Input/output (I/O) interfaces like USB, Ethernet (e.g., GigE Vision for 1 Gbit/s bandwidth), and wireless options ( or ) facilitate connectivity to external systems. Rugged housing encases these elements, often with IP67-rated protection against dust and moisture, ensuring durability in harsh environments. Typical specifications for 2025 smart camera models include resolutions up to 4K (3840x2160 pixels) for high-detail imaging, with power consumption optimized to low-voltage levels (e.g., 5-12V) suitable for battery-powered or edge deployments. All-in-one modules integrate these components into compact forms, often palm-sized (under 100mm in dimensions), reducing overall system footprint while maintaining performance. Hardware evolution has emphasized , progressing from bulky 1980s units with separate boards to 2025's single-chip CMOS-based designs that achieve palm-sized integration with advanced for sustained operation.

Software and Algorithms

Smart cameras incorporate embedded that serves as the foundational software layer, typically built on real-time operating systems (RTOS) such as or Zephyr to handle device , sensor interfacing, and low-latency operations essential for continuous . These systems prioritize deterministic scheduling and to meet the stringent timing requirements of vision tasks, enabling seamless integration of hardware components like sensors and processors without external dependencies. Core to their intelligence are algorithms, including the , which identifies boundaries by computing the magnitude at each as Gx2+Gy2\sqrt{G_x^2 + G_y^2}
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