Machine vision, or computer vision, encompasses the methods and technologies that enable machines to analyze and interpret images or video sequences from the real world. This field, situated at the intersection of artificial intelligence and signal processing, aims to reproduce the human ability to understand visual scenes. Unlike simple image acquisition, machine vision entails extracting relevant knowledge from visual data, allowing a machine to make decisions or act accordingly.
Use Cases and Examples
Machine vision is applied in object recognition, face detection, autonomous driving, industrial quality control, intelligent video surveillance, automatic license plate recognition, and medical imaging diagnostics. For example, it enables automated product inspection in manufacturing or early disease detection from medical images in healthcare.
Main Software Tools, Libraries, Frameworks
Key tools include the open-source library OpenCV, deep learning frameworks like TensorFlow and PyTorch, and specialized frameworks such as Detectron2, YOLO, and MMDetection. Comprehensive software solutions like Halcon and MATLAB are also widely adopted in industry.
Recent Developments, Trends and Evolutions
Machine vision is rapidly advancing with deep learning breakthroughs, enabling ever more accurate image recognition and understanding. Diffusion models and transformer architectures provide new capabilities for contextual analysis. The integration of machine vision into embedded systems and edge computing now enables real-time, on-site processing, enhancing data security and the responsiveness of intelligent systems.