Object DetectionSolutions
Models that find, count and locate specific objects in your images and video, trained on your own scenes and tested honestly before deployment.
Object Detection Solutions: what the work involves
Counting stock on shelves, checking whether workers wear helmets, tallying vehicles at a gate, finding damaged items in photos: tasks like these are done by people looking at images, one at a time. They are slow, they bore people quickly, and results differ between observers. General-purpose detectors know common things like cars and people but fail on your particular product, uniform or lighting.
We build a detector around your own footage. Images or video frames are collected from the real camera positions, labelled with bounding boxes for the objects that matter, and split so that testing uses scenes the model never saw. We fine-tune a YOLO-family model or similar, choosing size according to the hardware, from a phone or Jetson-class device to a cloud GPU. Evaluation reports precision and recall per class, with examples of what it misses and confuses. Post-processing adds counting lines, zones, tracking across frames and rules, for example helmet absent in zone. Output is delivered as an API, a dashboard or a stream of events, with a review screen for uncertain detections.
Core features
Custom class training
The model learns your products, equipment or situations, not just the generic categories of public datasets.
Held-out scene testing
Evaluation uses cameras, days and conditions kept out of training, so results reflect real deployment.
Counting, zones and tracking
Detections turn into counts, entry and exit events, time in zone and rule-based alerts.
Edge or cloud deployment
Models are sized for phones, Jetson-class devices or cloud GPUs, depending on latency and privacy needs.
Review screen
Low-confidence detections are queued with the image so a person can confirm, correct or reject them.
Class-level reporting
Precision and recall are reported per object type, including common confusions and failure examples.
What we get right before launch
Labelling effort
Quality labels are the main cost and the main driver of accuracy. We budget labelling time honestly, use pre-labelling to speed it up, and review samples for consistency.
Small, occluded or crowded objects
Tiny or overlapping items are hard to detect and count. We test on those cases explicitly, suggest camera changes that help, and report counts as estimates with known error ranges.
Privacy of people in frame
Footage with people raises consent and retention questions. We process on the edge where possible, blur faces if identity is unnecessary, and limit stored video.
Tools and technology
- Python
- YOLO
- PyTorch
- OpenCV
- NVIDIA Jetson
- FastAPI
- ONNX Runtime
- PostgreSQL
Common questions, answered
How many images are needed?
From a few hundred to several thousand, depending on how varied the scenes and objects are. Starting from a pre-trained model reduces the amount. A short pilot shows whether more data is worth collecting.
Can it run on a phone or a small device?
Yes, with compact models and optimisation, trading some accuracy for speed. We test on the target device early, since performance on a server does not predict performance on the edge.
How reliable are the counts?
Reliable enough for many operational uses, but not exact in crowded or occluded scenes. We measure counting error on your footage and report it as a range you can plan around.
Can it detect something it has never seen?
Not reliably. New object types need examples and retraining. Open-vocabulary models can help as a first pass, but for dependable results we train on your specific objects.
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Ready to start your Object Detection Solutions project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.