The “high-frequency object counting system,” relying on deep learning models, utilizes artificial intelligence to accurately and in real-time identify and count frequently occurring objects in images. This system, through deep analysis of image features, has the capability to count high-frequency objects with high precision and has revolutionized object counting tasks within the framework of deep learning.
This technology provides users with features that can be effectively utilized across various industries
DEMO version
🟢 Helps reduce errors and increase speed in counting tasks
🟢 Counting the number of people in a group photo
🟢 Counting batches of products on production lines and in warehouses
🟢 Counting the number of vehicles in traffic or parking lot images
🟢 Counting similar products on store shelves
🟢 Counting aquatic larvae in various applications
🟢 Counting the number of animals in an image
🟢 Counting the number of buildings in an urban area
🟢 Counting objects in medical images (e.g., counting cells in microscopic images)
🟢 Counting similar objects without the need for selecting a sample image
🟢 Ability to count even with partial overlap of objects
🟢 Real-time counting without prior training and independent of object type, utilizing deep learning models
🟢 Exceptional accuracy with user-adjustable sensitivity settings
🟢 Fast counting, approximately 3 seconds, without the need for high-powered hardware
🟢 Ability to count in images with complex backgrounds
🟢 User interaction capability to ensure accuracy of results
🟢 Counting range from 20 to 600 objects
🟢 Ability to count specific, unknown, or important objects from the user’s perspective to maintain privacy
