Object detection
Find, count, and classify objects in images and video — products on a shelf, vehicles in a lot, defects on a line, people in a frame.
AI LAB · 04
Vision systems that actually ship
Computer vision that works on real cameras, in real conditions.
Detection, classification, segmentation, OCR, and real-time video analytics — engineered for the edge cases that lab benchmarks ignore. We pick the simplest model that hits the accuracy bar, then we spend most of the project on labels, deployment, and the failure modes that matter.
What we build
Software that can see.
Six computer-vision capabilities we ship into production — from factory floors to mobile apps to medical workflows.
Image classification
Categorise images at scale — content moderation, medical screening, document type sorting, brand asset tagging.
Image segmentation
Pixel-level masks for product photography, medical imaging, satellite analysis, and creative tooling.
OCR & document vision
Extract structured data from receipts, invoices, forms, IDs, and handwritten notes — even at oblique angles.
Face & biometric
Face detection, recognition, liveness, and emotion analysis — with the privacy controls and consent flows the laws require.
Real-time video analytics
Edge and cloud video pipelines for retail traffic, manufacturing QC, sports analytics, and surveillance.
Use cases
Vision that earns its keep.
Three deployments where a camera replaced a clipboard — and the numbers got better, not just faster.
Defect inspection on a moving line
A custom YOLO model running on factory cameras catches surface defects on aluminium panels at 60 FPS — replacing manual QC that used to miss 1 in 8 defects.
99.2%detection rate
−84%QC labour
Shelf compliance & planogram
Brand reps photograph store shelves. The model identifies every SKU, compares it to the agreed planogram, and reports compliance to HQ within seconds.
92%SKU accuracy
12×audit speed
Radiology pre-read triage
A segmentation model flags suspicious findings in CT scans for the radiologist to review first. Not a diagnosis — a priority queue that shaves minutes off critical reads.
−43%time to flag
100%human-reviewed
The stack we use
Modern vision, classic engineering.
We use the latest foundation models where they help, but most production wins come from well-trained YOLO-class detectors with careful labelling.
Detection & segmentation
- YOLOv8 / YOLOv9
- Detectron2
- Segment Anything (SAM)
- OpenMMLab
Foundation models
- CLIP
- DINOv2
- Florence-2
- Google Vertex Vision
Frameworks
- PyTorch
- OpenCV
- TensorRT / ONNX
- MediaPipe
Annotation & data
- Roboflow
- Label Studio
- CVAT
- Synthetic data pipelines
How we work
Six steps to a production vision pipeline.
Data is the differentiator. We spend more time on labels and edge cases than on model architecture — because that's what actually moves accuracy in production.
01
Use-case scoping
Define what counts as a hit, a miss, and a false positive. The cost of each one shapes the model design.
02
Data & labels
Collect images, design a labelling guide, train labellers. This step is 60% of a successful CV project.
03
Model selection
Off-the-shelf, fine-tuned, or custom? We pick the cheapest option that hits the accuracy bar.
04
Train & evaluate
Iterate on a clean test set. Confusion matrix, per-class recall, edge cases — not just top-1 accuracy.
05
Deploy
Cloud GPU, on-device, or edge. We tune for latency, throughput, and cost depending on where it runs.
06
Monitor & retrain
New camera angles, new products, new failure modes. Drift detection plus a retraining pipeline keeps accuracy honest.
Frequently asked
Computer vision questions.
What is computer vision?
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Computer vision is the branch of AI that gives software the ability to interpret images and video — detecting objects, classifying scenes, reading text, recognising faces, and segmenting regions. At Appsmediaz, we build production computer vision pipelines that run on cloud GPUs, edge devices, or mobile phones.
Can I use computer vision in real-time applications?
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Yes. Modern object detection models like YOLOv8 and YOLOv9 run at 30 to 100+ frames per second on a single GPU, and quantised versions run on edge devices like NVIDIA Jetson or Coral. We pick the model and runtime based on your latency and cost constraints.
How accurate are computer vision models?
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Accuracy depends on the problem, the quality of training data, and how diverse the deployment conditions are. Well-engineered models routinely hit 95 to 99% accuracy on well-defined tasks. The most important step is honest evaluation on real-world data, not lab benchmarks.
How long does it take to build a computer vision system?
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A focused detection or classification project ships in 6 to 10 weeks. Projects involving heavy data collection, custom annotation, or edge deployment typically take 10 to 16 weeks. We start with a 2-week scoping sprint to validate feasibility and budget.
How much does computer vision development cost?
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Computer vision projects at Appsmediaz typically range from $15,000 for a focused single-task model to $100,000+ for multi-camera real-time pipelines with edge deployment. Annotation work is the biggest variable; we'll show you the breakdown.
Explore the rest of the AI Lab
Got a camera and an idea?
Send us a few sample images of what you want to detect. We'll come back with feasibility, a rough budget, and a candid view on whether it's worth doing.
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