Count every part with precision. Reduce delays, eliminate mix-ups, and improve throughput with automated object detection and classification powered by AI-driven vision systems.
Our Cognitive Product Inspection system, Kompass™, replaces manual processes with optical detection for error-free, efficient operations.
From automotive assembly to FMCG packaging, companies deploy object counting and sorting using computer vision to achieve unmatched quality standards. Explore case studies across industries where automated object detection and classification has eliminated rework and recalls.
.avif)
Engine Tappet inspection with AI vision and HURON hardware
.webp)
High-speed biscuit inspection with automated quality control:
.avif)
Labelled bottle inspection with AI vision system and modular hardware:
Object Counting and Sorting using Computer Vision is the process of using AI-powered cameras and object detection algorithms to identify, track, and count items in real time. It enables manufacturers and logistics companies to automate inspection, reduce errors, and handle high-volume operations with greater speed and accuracy compared to manual methods.
Automated object detection and classification eliminates human error by identifying and separating items based on predefined categories. Combined with object tracking and counting, it allows precise SKU handling, reduces bottlenecks, and ensures consistent throughput. This automation is widely used in industries such as FMCG, pharmaceuticals, and automotive to improve accuracy and productivity.
Weight-based methods often fail when dealing with micro-tolerances, leading to miscounts and inaccurate inventory data. By applying bounding box counting, segmentation-based counting, and density estimation in object counting, computer vision provides a far more reliable solution. It ensures every part is accounted for without delays or costly errors in shipping and packaging.
Yes. Computer vision systems are designed for counting items in video streams at conveyor speeds. Using image preprocessing for object detection and machine learning models for object counting, these systems detect overlapping or fast-moving objects accurately. This makes them highly effective for real-time monitoring in logistics and large-scale production environments.
Optical sorting technology uses high-resolution cameras and vision algorithms to separate defective, misaligned, or incorrect items from the production line. Combined with automated sorting in logistics and manufacturing, it enhances quality control, minimizes waste, and guarantees only the correct products move downstream, strengthening customer trust and reducing operational costs.
Modern machine learning models for object counting leverage deep learning to adapt to complex environments, varying lighting, and overlapping objects. Unlike rigid rule-based systems, they improve with data over time, offering scalable accuracy for object tracking and counting. This ensures consistency across industries that demand high precision, from packaging to warehouse automation.
.svg)