Machine vision and computer vision are terms we tend to use interchangeably, but these have slightly different connotations depending on the context. These two intertwined vision technologies both refer to enabling machinery with “eyes” to recognize, detect, and identify objects, but there are a few key components that differentiate the two.
What is Computer Vision?
Computer vision (CV) refers to the broader, interdisciplinary field of AI that emphasizes on training computers to automatically detect specific objects and formulate patterns through digital imagery and video content. The term computer vision typically refers to an academic branch of AI and machine learning.
The main objective for computer vision is to replicate the complexities of the human eye and extract vital data that can be used for machine learning algorithms. These algorithms are built into a model that is then pre-trained and stored in machine learning libraries. These machine learning libraries, like TensorFlow, can then be utilized by end users that can quickly and seamlessly incorporate their ideas with the pre-trained model for AI inferencing. With the advancement in AI and machine learning over several eras, computer vision has seen significant improvements in accuracy, confidence levels, usability, and overall innovation
Common Computer Vision Applications:
- Object Detection
- Image Classification
- Facial & Gesture Recognition
- Text Recognition (OCR)
- Robotics Guidance
What is Machine Vision?
Machine vision (MV) is a subsidiary of computer vision, where hardware and computer vision applications are incorporated into machinery to enhance real-world processes and operations. The encompassing goal of machine vision is to synergize IoT sensors, AI computer vision software, and machine vision systems to “see”. It precisely automates and enhances the efficiency of certain tasks all in real-time. The industrial sector is one of the pioneers that primarily demanded and first implemented machine vision technologies into their production lines. This resulted in outstanding performance improvements and significant bottleneck reductions.
Common Machine Vision Applications:
- Quality Inspection
- Defect Detection
- Object Sorting & Handling
- Predictive Maintenance
- Security & Surveillance
- & More
What Are the Differences Between Computer Vision and Machine Vision? (Computer Vision vs Machine Vision)
The difference between computer vision and machine vision is that machine vision utilizes computer vision technologies to drive enhanced processes in existing or new systems. The main reason why these two vision technologies are occasionally used interchangeably is because machine vision is a subset or discipline of computer vision. The term computer vision is typically used in the context of AI academics and deep learning procedures, while machine vision refers to a completed system solution incorporating a vision AI application.
In addition to machine vision being a branch of computer vision, there are also some key distinctions on how the two vision AI technologies are utilized:
Deep Learning Training vs AI Inferencing
Computer vision applications typically apply to deep learning training since its primary goal is to develop accurate dataset models. Machine vision applications use AI inferencing which take a trained preset model and implement new data for real-world deployments. For example, computer vision can be seen as a university professor who provides its students with knowledge and fundamentals. From there, the new graduates take their refined knowledge to implement into their careers.
Machine vision applications are demanded to intake vast amounts of data to process in real-time in comparison to computer vision, which only processes a predetermined dataset that a professional has meticulously prepared.
The two vision AI technologies share identical deployment verticals because of market demands. Development of precise and confident datasets from computer vision are required for machine vision applications to successfully deploy and operate. There are numerous deployment verticals that are incorporating machine vision today. From industrial automation to the energy sector, machine vision has become widely used to significantly improve the efficiency of tasks and processes.
The Purpose of Machine Vision
Traditionally, workers would physically inspect and exam each asset or product on the assembly/production line to ensure it meets all standards. This process of quality control can become the primary bottleneck of production due to the criticality and timely nature of it. With machine vision, IoT devices and machine vision systems work together to automate QC processes at superhuman accuracy and speed. There are even some applications where machine vision can identify defects, patterns, and anomalies that are naked to the human eye! Machine vision is an innovative technology that is revolutionizing nearly all market verticals with remarkable performance capabilities.
Enabling Machine Vision At The Rugged Edge
Two major challenges recognized when deploying a computing solution for on-prem machine vision are reliability and processing power. Prioritizing deployment reliability is critical before considering any kind of real-time computing. At the rugged edge, incompetent computing solutions are prone to unstable power conditions, extreme factory conditions, and exposure to unconventional environments. This is why industrial computers play a vital role in enabling machine vision applications. Our high-performance flagship Industrial GPU computers, such as our AI Edge Inference Computer (RCO-6000-RPL) and Machine Vision Computer (VCO-6000-RPL), are specifically designed to withstand and remain operational under varying factory conditions.
In addition, machine vision applications require high-performance computing for low-latency analytics and real-time processing. This comes in the form of performance acceleration through formulating a heterogeneous computing approach. Industrial computers utilize a heterogeneous approach with the implementation of CPUs, GPUs, TPUs, and other domain-specific architectures to construct an optimized system for effective data consolidation and acquisition.
Hardware Technologies for Machine Vision Applications
Heterogenous computing within Industrial GPU computers are the primary drivers of machine vision. By combining meticulously selected components, edge computing manufacturers like Premio Inc. can construct a system specifically optimized for machine vision applications. Each component is required to meet some level of industrial-grade reliability and performance for these complex edge vision AI workloads.
Specialized Hardware Accelerators for Machine Vision:
- CPU: sequential processing for task management and single-threaded workloads. Semiconductor manufacturers have developed specific CPU models that have been tailored to embedded deployments. These embedded models leverage high-performance at low TDP and have extended product lifecycle management.
- GPU: the powerhouse for complex machine learning workloads with parallel processing. Workstation-class GPUs have been designed for edge AI acceleration with emphasis on performance in TOPS and stabilized driver roadmaps.
- TPU: designed to process lite AI inferencing workloads in an ultra-compact m.2 form factor with minimal power consumption. Ideal for enabling AI workloads in space-constrained deployments.
- NPU: integrated AI acceleration at the microprocessor level. Semiconductor manufacturers have recognized the significance of machine learning and have implemented a dedicated low-power AI engine within their processors.
PCIe for GPU Support, High-Speed I/O, and Flexible Expansion
PCIe plays a vital role for industrial GPU computers in powering and enabling machine vision applications. It is directly linked to heterogenous computing because it provides the capabilities to incorporate core hardware accelerators into the system. By delivering high-speed data throughputs with low latency and establishing standardized compatibility with hardware accelerators, PCIe has allowed various components to synergize and work cohesively to drive complex AI workloads like machine vision.
In addition, PCIe has paved the way for near limitless scalability and upgradability for I/O expansion, additional GPU support, and much more.
Importance of Memory (RAM)
High-speed memory, or RAM, is needed to enable real-time processing for machine vision applications. Memory temporarily stores large volumes of data for immediate access and usage. Industrial GPU Computers utilize DDR SODIMM memory for enhanced ruggedness and allow for a compact footprint. In some industrial computers, ECC memory is supported for extended operational reliability; prolonging uptimes and reducing possibilities of system failure.
Connectivity for IoT Sensors and Cameras
I/O connectivity is critical to driving machine vision applications and is often overlooked. Industrial GPU computers are designed to provide compatible I/O for IoT cameras and sensors to connect and seamlessly process the raw data for real-time insights. USB and GigE are common vision camera interfaces that offer an optimized balance between:
- Bandwidth speed
- High-resolution imagery
- PoE Support
- Cable length reliability
These connection interface features are vital to the overall performance and reliability of relaying quality data for AI analysis. Since machine vision systems vary in deployment specifications, it is important for the industrial computer to offer flexibility to meet all sorts of I/O configurations. Whether it be PoE support, M12 connection types, USB 3.2 (10 Gbps) ports or even high-speed networking, industrial GPU computers should have the flexibility to meet those requirements through either fixed on-board I/O or Premio’s EDGEBoost I/O modules.
Industrial GPU Computers Powering Machine Vision Systems
Premio has multiple flagship product series specifically tailored to be incorporated into a full machine vision system. Each product series acts as industrial building blocks that allow system integrators and OEM/ODM developers to select the most optimized edge solution for their deployment requirements. From I/O and performance acceleration customization with EDGEBoost technologies found in our RCO-6000 Series to full-height, full-length dual GPU support in our VCO-6000 Series, we provide reliable rugged edge computers for various machine vision applications.