Industrial vision systems for defect detection and object recognition
Cameras that see more accurately than the human eye
Computer vision AI development is an R&D service that automates visual inspection on industrial sites through deep-learning-based defect detection, object recognition, and behavior analysis. TreeSoop’s KAIST-trained vision researchers deliver 99.7% defect-detection accuracy and 30FPS real-time processing with state-of-the-art models such as YOLOv8, EfficientDet, and Segment Anything, applying few-shot learning that trains from as few as 50 images. Proven deployments across manufacturing, logistics, and security; rated 4.92/5 on Wishket, Korea’s largest dev-outsourcing platform.
When quality inspection on the production line is done by eye, accumulated fatigue drives up the misdetection rate — and staffing night shifts keeps getting harder.
Sorting and tagging camera footage by hand consumes enormous time and money — often enough to stall AI adoption before it even starts.
Legacy OpenCV rule-based systems need reconfiguration every time lighting shifts, a new defect type appears, or the product changes — driving up maintenance costs.
State-of-the-art object detection models such as YOLOv8 and EfficientDet, combined with anomaly detection techniques, catch defects down to the 0.1mm level in real time.
Few-shot learning trains models from just a few dozen sample images, dramatically cutting data-collection costs and training timelines.
Model compression (quantization, pruning) and TensorRT optimization deliver 30FPS+ real-time inference on industrial cameras and edge computers.
An automated pipeline spanning data collection, training, evaluation, deployment, and monitoring keeps model performance improving continuously.
We analyze the detection targets, camera environment, and accuracy requirements, then collect the initial dataset.
Precise labeling with professional annotation tools, followed by dataset expansion through data augmentation.
We experiment across multiple architectures and select the model that best meets the precision/recall targets.
Pilot operation on the actual production line to validate robustness against environmental variation.
After edge-device deployment, we monitor for performance drift and ship regular model updates.
A vision system that automatically detects scratches, particles, and pattern errors on semiconductor wafers at 10+ images per second
A system that detects foreign objects, discoloration, and shape defects in food on the conveyor belt in real time and rejects them automatically
Automatic recognition of barcodes, QR codes, and package labels at 99.9% accuracy across varied angles and lighting conditions
99.7%
Average detection accuracy
Measured across industrial deployments
30FPS+
Real-time processing speed
On edge devices
50 images
Minimum training data
With few-shot learning applied
3 months
Average development timeline
From PoC to on-site deployment
Our experts will recommend the best approach for your project.
