Shanghai Civil Engineering Society
Computer vision refers to the process of enabling machines to simulate human vision through visual information such as digital images or videos, achieving understanding, recognition, classification, tracking, and reconstruction of objects. It is a branch of artificial intelligence that involves multiple fields including image processing, pattern recognition, machine learning, and deep learning.
Asartificial intelligenceandmachine learningalgorithmshave entered a stage of deep integration with industry, machine vision technology has been widely applied in scenarios such as facial recognition, autonomous driving, drones, medical image analysis, and industrial production. It mainly utilizes the following six mainstream machine vision technologies. Let’s explore them together~
Image Classification
Image classification is a method of image processing that distinguishes different categories of targets based on the different features reflected in the image information. It uses computers to perform quantitative analysis of images, categorizing each pixel or region of the image into one of several categories, replacing human visual interpretation.
Common Methods:Color feature-based indexing techniques, texture-based image classification techniques, shape-based image classification techniques, spatial relationship-based image classification techniques, etc.
Main Applications:Scene classification, object recognition, image annotation, medical imaging, industrial inspection, and security monitoring, etc.
Object Detection
Object detection refers to the task of identifying the location of target objects in images or videos and labeling their respective categories. Compared to image classification tasks, object detection requires accurate identification of the location and number of targets, making it more difficult but also more practical. In practical applications, different models and algorithms can be selected based on specific scenarios and needs to achieve tracking, recognition, and analysis tasks.
① Faster R-CNN: A target detection model based on deep neural networks that improves detection speed by introducing anchor points in the Region Proposal Network (RPN) and uses the RoI Pooling layer to achieve detection of targets of different sizes.
② YOLO (You Only Look Once): A model based on a single-stage object detection algorithm that transforms the object detection task into a regression problem, predicting the category and location of targets through convolutional neural networks.
③ SSD (Single Shot MultiBox Detector): Also a model based on a single-stage object detection algorithm that applies different sizes and shapes of prior boxes on each feature layer to detect targets of different scales.
① Intelligent Security: Monitoring personnel and vehicles in scenes to achieve target tracking and recognition.
② Autonomous Driving: Achieving autonomous driving by recognizing road signs, traffic lights, pedestrians, and other vehicles.
③ Drones: Identifying and tracking targets in the flight area of drones to achieve intelligent control and navigation.
④ Industrial Manufacturing: Inspecting and classifying products during the production process to improve production efficiency and quality.
⑤ Medical Diagnosis: Assisting doctors in diagnosis and treatment by identifying and locating tumors and other abnormalities in medical images.
Especially suitable for intelligent applications at the edge, such as in the scenario-based solutions of Yingma Technology, which primarily achieve real-time perception, target identification, monitoring, and early warning of long-tail scenarios through edge computing boxes combined with machine vision, big data, and other technologies, assisting in the intelligent upgrade of fields such as transportation, campuses, construction sites, and chemical parks to reduce costs and increase efficiency.
Object Tracking
Object tracking refers to the task of tracking the position and shape information of a known initial target in subsequent frames through feature extraction and tracking algorithms in a video sequence.
① Correlation filter-based tracking methods: Calculate the correlation between the target and the template, and the result can represent the target’s position in the current frame.
② Particle filter-based tracking methods: Randomly generate multiple particles around the target, predict these particles based on the target’s motion model, and update the weights of the predicted particles using observation information to select the particle with the highest weight to represent the target’s position.
③ Deep learning-based tracking methods: Use deep learning algorithms for feature extraction and representation of the target, and predict the target’s position in the current frame based on its position and features in the previous frame. Common deep learning tracking algorithms include Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN).
Main Applications:Object tracking technology is applicable in fields such as video surveillance, autonomous driving, and intelligent transportation, enabling real-time tracking and recognition of targets for automated control and intelligent analysis.
Semantic Segmentation
Semantic segmentation aims to label each pixel in the input image as belonging to a specific semantic category. Unlike object detection and image classification, semantic segmentation can not only recognize objects in the image but also assign labels to each pixel, providing a more detailed and accurate understanding of the image.
Common Models:FCN (Fully Convolutional Network), U-Net, DeepLab, etc. In recent years, many new semantic segmentation models based on deep learning have emerged, such as PSPNet and DeepLab V3+, which have improved in terms of accuracy and efficiency.
Main Applications:Semantic segmentation is suitable for scenarios that require fine segmentation and pixel-level classification of images, such as road segmentation in autonomous driving, lesion segmentation in medical images, and land classification in geographic information systems.
Instance Segmentation
Instance segmentation is a higher-level task that combines object detection and semantic segmentation. It aims to detect objects in images while segmenting each object into precise pixel-level regions. Unlike semantic segmentation, instance segmentation can not only segment different categories of objects but also separate them into independent, pixel-level regions.
Common Models:Mask R-CNN, FCIS (Fully Convolutional Instance-aware Semantic Segmentation), SOLO (Segmenting Objects by Locations), etc.
Main Applications:Instance segmentation is suitable for scenarios that require fine segmentation of images and differentiation of different objects, such as pedestrian and vehicle segmentation in autonomous driving, organ segmentation in medical images, and building segmentation in remote sensing images.
The above five key machine vision technologies can assist computers in extracting, analyzing, and understanding useful information from single or multiple images, empowering various industries to realize AI applications and build a smarter, more beautiful visual world.
Note: The above popular science content is sourced from online compilations.
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Guangzhou Yingma Information Technology Co., Ltd. was established in 2006 and is an artificial intelligence company dedicated to providing AIoT products and segmented scene solutions that enable “cloud-edge-end” collaboration.
The “Deep Yuan” AI product system under Yingma has created a full-stack AI application service architecture based on high, medium, and low-level computing hardware, driven by algorithm self-training and ecological integration, supported by an AI empowerment platform, and assisted by a toolchain, connecting the full chain from scene demand to algorithm, hardware integration, business platform docking, and project delivery, providing customers with dual customizable products and services for algorithms and computing power, promoting the widespread application of AI and edge computing in segmented scenarios.
Yingma’s AIoT products and customized services target various industries and segmented scenarios such as smart cities, smart transportation, smart finance, smart campuses, smart emergency response, and smart parks, providing comprehensive software and hardware support and product customization capabilities for clients.
Yingma Technology’s core philosophy of “perceiving everything, intelligent computing empowerment” fully empowers the intelligent transformation of various industries, constructing an all-encompassing intelligent world.
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