COMPUTER VISION

YOLO (You Only Look Once)

Computer Vision

Definition

YOLO is an extremely fast object detection algorithm that analyzes an entire image in a single pass. The latest versions (YOLOv8, YOLOv9) combine speed and accuracy, enabling real-time detection on video at 30+ fps even on modest hardware. It is the de facto standard for real-time applications such as production line monitoring.

Related terms

Computer Vision

Object Detection

Object detection is the computer vision technique that identifies and locates specific objects in an image or video, drawing a bounding box around each one. It is used to count products on a conveyor belt, detect defects in components, and monitor safety areas. Algorithms like YOLO enable real-time detection, even on edge devices.

Computer Vision

CNN (Convolutional Neural Network)

CNNs are neural networks specialized in image analysis, inspired by the biological visual system. They use convolutional filters to automatically detect features such as edges, textures, and complex shapes. They underpin nearly all industrial computer vision applications, from quality control to defect recognition in products.

Computer Vision

Edge AI

Edge AI is the execution of AI models directly on the device (camera, sensor, machinery) instead of on cloud servers. It eliminates network latency, works offline, and protects data privacy. In factories, it enables real-time quality inspections on the line without depending on internet connectivity. Devices like NVIDIA Jetson make edge AI accessible.

EXPLORE

More terms in Computer Vision

Computer Vision

Computer vision is the field of AI that enables computers to interpret images and video. It includes object recognition, quality inspection, license plate reading, and security video analysis. In Italian manufacturing, computer vision automates visual quality control, reducing defects reaching the customer and speeding up production lines.

Object Detection

Image Classification

Image classification is the task of automatically assigning a category to an image. For example: 'conforming part' vs 'defective part', or automatically classifying scanned documents by type. CNNs (Convolutional Neural Networks) achieve accuracies above 99% on many industrial classification tasks.

Segmentation

Image segmentation is the technique that classifies every single pixel of an image, precisely identifying the contours of each object. Unlike object detection which draws bounding boxes, segmentation delineates the exact shape. It is used for precise measurements, medical image analysis, and autonomous guidance of robots and vehicles in factories.

Face Recognition

Face recognition is the technology that identifies or verifies a person's identity by analyzing facial features. In businesses, it is used for access control, attendance tracking, and security. It is important to implement it in compliance with GDPR and European AI regulations, which place strict limits on the use of biometric recognition.

Quality Inspection

AI quality inspection uses computer vision to automatically verify product conformity on the production line. High-resolution cameras and deep learning algorithms detect scratches, dents, color defects, and dimensional irregularities with speed and consistency impossible for the human eye. It reduces returns and rework costs.

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