What is image recognition used for?
Image recognition is a computer vision technique that allows machines to understand what they “see” in visual sources, such as images and videos. Image recognition is capable of detecting and classifying places, people, objects, actions, and many other types of elements within these sources, and drawing inferences by analyzing them. Using Skyl.ai AI Platform for computer vision you can quickly build and deploy high quality Image Recognition models in hours.
Image Recognition Industry use case:
Shelf Management in Retail
Every year, retailers lose billions in sales due to products being out of stock and improper shelving practices. Ensuring a good retail experience is crucial to winning customer loyalty and increasing customer value. AI and ML can be leveraged to automate the tedious manual gap check process by identifying all availability issues on shelves. Visual scanners aid in recognizing product locations, products that are out of stock and those that are past their best by date.
Insights from Property Listing Images
Reviewing large amounts of real estate listing images to extract attributes of the property is a tedious, manual task. Computer vision can analyze these images to automatically extract specific attributes like locality, decor, flooring type, types of rooms, and living space conditions. This assists real estate platforms to get a more automated evaluation of the property, cross-reference this data with existing information about the locality or area, and check for inconsistencies in the listing.
Medical Imaging & detection
Deep Learning has demonstrated remarkable progress in image-recognition functions. Medical imaging is one of the most performed tasks using this technology. AI methods excel at recognizing complex patterns in the images and providing assessments of medical characteristics. AI models can be an effective tool for analyzing medical ailments like cardiovascular abnormalities, lung diseases like pneumonia, the development of tumors and melanoma, and checking for fractures from high-resolution medical imagery. This helps to provide timely treatments to patients.
Fleet Safety (prevention)
Accidents are an almost unavoidable event when it comes to using automobiles. AI models are used to reduce chances of accidents occurring and ensure the safety of the driver and fleet. Using Object detection models on driving video telematics can track risky driving practices and irresponsible driver behavior.
Inspection is usually the first step in a damage insurance claims process, whether it’s an automobile, mobile phone or property. Assessing the damages to calculate an estimate of repair costs can be a challenging task for insurance providers. Deep Learning models can be used to detect the different types, area, and severity of damage with greater accuracy and automate the claims process.
Try out these ML models
Social Media Photo Categorization
Categorizing social media photos by food, people, and locations
Pneumonia Detection in X-Rays
Image classification technology to aid radiologists
Real-Estate Listing Photos
Categorize different rooms in the home!
Advertisement Sentiment Analysis
Understanding ads sentiment and topics
Ecommerce Product Categorization
Classify different aspects of clothing
Detect Equipment on a Surgical Tray
AI-assisted equipment detection on a surgical tray using computer vision
AI-assisted watermark detection from images using computer vision
Metal Surface Defect Inspection
Inspect and detect defects in metallic surface using AI
AI assisted Gastrointestinal Endoscopy
Computer-assisted detection (CADe) using Computer Vision
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