Vision Systems
at the Edge
Embedded computer vision for industrial and IoT applications. Image classification, object detection, and visual anomaly detection running locally on constrained hardware — no image transmission, no cloud latency, no data exposure.
The Case for Local Vision
Transmitting a camera stream to the cloud for inference introduces substantial latency, significant bandwidth cost, and — in many industrial and enterprise environments — an unacceptable privacy exposure. For a system requiring 30ms response time, cloud inference is not a design option.
Edge vision systems process images on the device. The output is a structured result — a detection flag, a classification label, a bounding box coordinate — not a raw image stream. This dramatically reduces data transmission requirements while preserving the privacy of the captured scene.
WIRL Engineering designs complete edge vision systems: hardware selection, camera interface, image preprocessing pipeline, model development and optimization, and deployment on the target MCU or SoC. The system is engineered as a whole, not assembled from disconnected components.
Presence & Occupancy Detection
Person detection and zone monitoring using low-resolution imaging — suitable for privacy-sensitive environments.
Visual Quality Control
Defect detection and dimensional inspection on production lines, running locally on the inspection hardware.
Object Classification
Category identification for sorting, inventory, and logistics applications with real-time output requirements.
Anomaly Detection
Visual deviation detection from known-good baselines — surface defects, assembly errors, contamination.
Access Control
Recognition and authorization systems requiring local inference for latency and privacy compliance.
Environmental Monitoring
Visual sensing of environmental conditions — fluid levels, smoke, obstruction — in remote or unmanned locations.
- Image sensor selection and interface (parallel DVP vs CSI-2)
- PSRAM requirement analysis for frame buffer storage
- Camera pipeline design: capture → preprocess → infer → output
- Resolution and frame rate tradeoffs against inference latency
- Lighting design for consistent model performance
- Thermal management for continuous vision workloads
- Hardware accelerator selection (NPU, DSP, dedicated CNN engines)
- End-to-end vision pipeline design and implementation
- Dataset preparation and augmentation strategies
- Model architecture selection for target hardware constraints
- Transfer learning from pretrained vision foundations
- Quantization and optimization for MCU/SoC deployment
- Camera hardware selection and integration
- Image preprocessing pipeline (crop, resize, normalize)
- Post-processing: bounding box decode, NMS, classification output
- Performance benchmarking on target hardware
Vision Intelligence
On Your Hardware
Describe your vision application and hardware constraints. We will define the right architecture.