This has greatly contributed to the widespread adoption of smart cameras, primarily for barcode reading, OCR reading, robot guidance, and feature detection. The fundamental characteristics of smart cameras (such as ease of use and cost-effectiveness) make them ideal for these applications where speed requirements are not high and space is limited.
Like any technology, the widespread adoption of smart cameras is subject to certain limitations and obstacles: limited computing power and poor software flexibility. These two factors interact, primarily due to the constraints of their compact form factor, preventing the use of faster chips like in PCs and hindering the easy porting of computationally intensive algorithms. However, with advancements in System-on-Chip (SoC) and embedded technologies, these limitations will undoubtedly be overcome.
Smart cameras can be used for single-point and multi-point detection applications.
In essence, single-point detection, rapid deployment, and ease of maintenance will remain advantages of smart cameras over PC-based vision systems, even as the dividing lines become more blurred in certain situations. Smart cameras and PC-based vision systems complement each other in several ways. With the increasing processing power of embedded systems, smart cameras may be able to handle faster and more complex applications that were previously unmanageable. Furthermore, at some point, multiple smart cameras can be used for multi-view applications if the software allows for a master-slave architecture, a traditional domain for PC-based multi-camera systems. In addition, IP67 protection against harsh environments and a compact form factor suitable for smaller spaces undoubtedly give smart cameras a significant advantage.
Technological Trends in Smart Cameras
In the past few years, with the popularization of smart cameras in various industries, we have seen several major trends in the development of smart cameras.
01 Application-Specific Smart Cameras - Low Cost and Single-Function Oriented
Over the past few years, we have seen two major trends in smart cameras. The first trend is that smart cameras are increasingly focused on specific applications or single functions, much like traditional optical sensors. For example, at extremely low cost, cameras may be used only for barcode reading in logistics tracking, identifying the presence/absence of certain features, or feature matching for robot guidance.
02 Smart cameras focused on artificial intelligence
The second trend is that smart cameras are leveraging more onboard intelligence or smarter embedded technologies to solve more complex inspection tasks. These tasks typically require more computing power, a limitation previously imposed on smart cameras by their compact form factor, but this is changing. A new development in this area is the emergence of artificial intelligence (deep learning), which has played a significant role in driving new AI-focused smart cameras. Using AI trainers such as Astrocyte from Teledyne DALSA facilitates time-consuming image training on high-end PCs with more powerful GPUs. AI-driven smart cameras will certainly overcome the limitations of past smart camera models.
It's worth noting that all these evolutions are driven by software advancements. Functional modularity is related to changes in software. AI-powered smart cameras have also been propelled by significant software technological breakthroughs. Of course, advancements in hardware and computing power, and even sensors, also actively contribute to the evolution of smart cameras. But software progress plays a crucial role in these technological developments.
03 Wireless-managed or cloud-based smart cameras
Another advancement in smart camera development is the integration of wireless technology for remote monitoring and control. While Wi-Fi may be unreliable or slow enough for image transmission or real-time monitoring due to data security or reliability concerns, remote limited inspection, control, and management by operations managers will become a common feature of smart cameras. Cloud-based smart terminal cameras are also emerging, leveraging cloud computing technology. The reliable and fast transmission of images and data using 5G technology is becoming a focus in many production workshops of major manufacturing centers.
Future Development Direction of Smart Cameras
From technology to supply chain decoupling, things seem to be moving in multiple directions. Technologically, the development of smart cameras always prioritizes faster, easier, more economical, and more powerful solutions. The advent of 5G makes cloud computing more realistic. Cameras with centralized computing in the cloud will make industrial cameras more popular. However, no matter how fast 5G technology becomes, data security and reliability, as well as real-time processing without any time delays, will always be a concern, and the burden and cost of main servers cannot be ignored. Edge computing with improved SoCs will make smart cameras more attractive in many situations. Furthermore, supply chain decoupling means that manufacturing will continue to be dispersed across different countries around the world, creating more opportunities for production automation using robots, which is perfectly suited to the ease of use and simpler wiring of smart cameras.
Speaking of smart cameras themselves, smart cameras with artificial intelligence capabilities, or so-called "inference smart cameras," will become increasingly popular, as will smart 3D cameras. In other words, as the capabilities of smart cameras improve, their applications will extend to more areas previously dominated by PC-based vision systems in the foreseeable future.
