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Cameras are already installed on production lines, loading docks, vehicles and retail floors. Computer vision turns that footage into structured events: a defect found, a pallet counted, a safety zone breached, a label read.
Success depends as much on optics, lighting, data collection and edge hardware as on the model itself. We engineer the full system, from image capture and annotation to model training, edge deployment and the integration that turns a detection into an action, and we validate performance under the real conditions where it will operate.
商業應用
- 01
Visual quality inspection
Detect surface defects, assembly errors and missing components on production lines, flagging items for rejection or manual inspection and retaining images to support root cause analysis.
- 02
Logistics and yard operations
Read container numbers, license plates and labels, count and classify goods at receiving docks, and track trailer and asset movements to keep inventory and yard management systems current.
- 03
Workplace safety monitoring
Identify protective equipment use, restricted zone entry and unsafe proximity between people and vehicles, alerting supervisors in near real time, with privacy measures designed in from the start.
- 04
Retail and field asset analytics
Monitor shelf availability and planogram compliance in stores, or analyze images captured by field technicians and drones to assess the condition of equipment, infrastructure and installations.
工程方法
- 01
Study the scene
Assess camera placement, resolution, lighting, motion and environmental conditions on site, because these physical factors often limit performance more than the choice of model does.
- 02
Build the dataset
Collect representative images across shifts, seasons and edge cases, define clear annotation guidelines, and label data with quality review to produce reliable training and test sets.
- 03
Train and benchmark
Fine-tune detection, segmentation or classification models, and benchmark accuracy, latency and failure modes on a held-out test set captured under real operating conditions.
- 04
Deploy at the right tier
Run inference on edge devices, on-premises servers or the cloud according to latency, bandwidth and privacy requirements, using optimized models with remote update capability.
- 05
Integrate and monitor
Send detections to MES, WMS, safety or maintenance systems as structured events, and monitor accuracy over time as cameras, products and site conditions change.
技術與平台
Models and libraries
- PyTorch
- Ultralytics YOLO
- Detectron2
- OpenCV
- Segment Anything
- Hugging Face Transformers
Edge and inference
- NVIDIA Jetson
- NVIDIA DeepStream
- ONNX Runtime
- TensorRT
- OpenVINO
Data and annotation
- CVAT
- Label Studio
- Roboflow
- FiftyOne
Platform and integration
- Kubernetes
- AWS IoT Greengrass
- Azure IoT Edge
- MQTT
- Apache Kafka
所提及之技術名稱均為其各自所有者之商標，並不代表任何合作或背書關係。
您將獲得的成果
- Site assessment covering cameras, lighting and infrastructure
- Annotated dataset with labeling guidelines and test set
- Trained models with benchmark results under real conditions
- Edge or cloud inference deployment with remote update capability
- Integration with operational systems and alerting
- Privacy considerations and data retention configuration
決策者關心的問題
Can we use our existing cameras?
Often, but not always. Existing cameras may lack the resolution, frame rate, angle or lighting a specific task needs. We assess your current installation early and test models on footage from it. Where hardware changes are needed, we specify them and explain the reason before you invest in new equipment.
How do you handle privacy in video analytics?
We design for privacy from the outset: processing at the edge where possible, blurring or discarding faces when identity is not required, limiting retention and restricting access to footage. Applicable laws differ by jurisdiction and use case, so we work with your privacy and legal teams to confirm requirements before deployment.
What if the model makes a wrong detection?
Every vision model produces some false positives and misses. We measure these rates on your data, tune thresholds to reflect the relative cost of each error type, and design workflows where consequential detections are confirmed by a person. Monitoring shows when performance changes so the model can be retrained.