Computer Vision Use Cases
Discover How AI is Transforming Industries.
Automated Gem Evaluation
Computer Vision
Gemstone and Jewelry Industries
Streamlines grading for gem dealers, jewelers, and certification labs, ensuring faster, more accurate assessments and boosting productivity in gem evaluation and quality assurance.
Automating the gem classification process, which involves evaluating shape, quality, and color, is traditionally slow, costly, and heavily reliant on human expertise.
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Our approach integrates 2-10 high-resolution cameras and a powerful GPU with sophisticated software, including an image classification neural network and specialized image processing techniques.
This system achieves precision rates of 85% to 97% depending on the specific task and reaches over 80% automation, saving thousands of man-hours annually and significantly enhancing operational efficiency.
Automated Object Size and Count Estimation System for Conveyor Belts
Computer Vision
Manufacturing and Mining Industries
Enhances process quality and efficiency by automating object size and count estimation on conveyor belts.
Manual analysis is infeasible due to fast-moving conveyor belts and numerous objects.
Difficulty in evaluating the process quality of previous stages.
Need to estimate sizes of various objects (particles, ore, bubbles) and calculate relevant statistics.
Hardware: high-resolution camera positioned above the conveyor belt and additional lighting to improve image clarity. Optional depth cameras (RGB-D) for enhanced analysis.
Software: image segmentation neural network for identifying and distinguishing objects. Postprocessing algorithms to refine and analyze data for size and count estimation.
Achieves around 90% accuracy in distribution estimation, depending on object disposition and intersections.
Achieves up to 98% accuracy in count estimation, also influenced by the disposition of objects.
Provides reliable statistics and insights to improve process quality and operational efficiency.
High-Precision Object Detection for Scrap Sorting
Computer Vision
Recycling and Waste Management
Enhances safety and profitability by identifying hazardous and valuable items in scrap with over 95% accuracy.
Identifying dangerous items like projectiles and gas balloons within scrap material.
Detecting valuable objects such as precious metals and jewelry.
The need for high accuracy in detection to ensure safety and maximize recovery of valuable items.
Balancing the cost of implementation with the benefits of improved sorting precision.
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Hardware:
Integrate one or more high-resolution cameras to capture detailed images of scrap material. Utilize a GPU to process image data.
Software:
Deploy an advanced object detection neural network trained to recognize and differentiate between hazardous and valuable items within scrap.
Continuous learning capability to improve detection accuracy over time.
Real-time processing to enable immediate identification and sorting.
Safety:
Significantly reduces the risk of handling hazardous items by accurately detecting dangerous objects.
Efficiency:
Detects around 80% of objects of interest with over 95% precision, ensuring high recovery rates of valuable items.
Cost-effectiveness:
Improves the profitability of recycling operations by maximizing the extraction of valuable materials.
Reduces downtime and labor costs associated with manual sorting and identification.
Advanced Paper-Sheet Tear Prediction
Computer Vision
Paper Manufacturing
Reduces production costs by predicting and preventing paper-sheet tears with over 92% accuracy.
High costs associated with paper-sheet testing, accounting for up to 2% of revenue.
The need to predict and prevent tears in paper sheets to minimize waste and production downtime.
Combining imaging data with time-series data to accurately forecast defects.
Ensuring that the predictive system is reliable and integrates seamlessly into existing production lines.
Hardware:
Multiple high-resolution cameras to continuously monitor paper sheets during production and GPUs to handle intensive data processing from imaging and time-series inputs.
Software:
Object detection and image segmentation neural networks to identify potential weak points in paper sheets.
Time-series modeling to predict the likelihood of tears based on historical and real-time data.
Real-time alerts to operators for proactive intervention.
Precision:
Achieves high precision (92%+) in detecting sheet defects within a 5-minute window.
Cost Savings:
Reduces costs associated with paper-sheet testing and waste, contributing to significant savings.
Efficiency:
Minimizes production downtime by enabling timely interventions before tears occur.
Enhances overall production quality and consistency, leading to better end-product reliability.
Automated Steel-Bar Counting System
Computer Vision
Manufacturing and Construction
Enhances safety and profitability by identifying hazardous and valuable items in scrap with over 95% accuracy.
Steel bars often need to be sold by count rather than by weight, depending on client requirements.
Counting bars on a rolling mill is difficult due to high speeds and frequent intersections.
The need to halt production at the correct moment to form proper packs of bars.
Ensuring accuracy and reliability in the counting process to meet client specifications and avoid overproduction or shortfall.
Hardware:
1-2 high-resolution cameras positioned to monitor the rolling mill and GPU to handle real-time image processing and data analysis.
Software:
Object detection and image segmentation neural networks to identify and count individual steel bars.
Post-processing algorithms to refine and verify counts, reducing errors from intersections and overlaps.
Real-time processing and communication with mill control systems to stop production at the precise count.
Accuracy:
Achieves around 99.5% counting accuracy, equating to approximately one mistake per two packs.
Efficiency:
Ensures production halts at the exact required count, optimizing packing and meeting client specifications.
Cost-Effectiveness:
Reduces labor costs and errors associated with manual counting.
Enhances overall productivity by minimizing downtime and improving operational precision.
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