Object detection
Locate and count defined objects under specified camera conditions.
Computer Vision Development
Image and video systems for detection, segmentation and visual inspection, integrated with the cameras and review tools your operation uses.
Inspection camera / frame 017
Label region
Review imageGlare crosses the barcode
Tools & technologies
- OpenCV
- Python
- Vision models
- Camera or upload APIs
Make images useful to an operation.
Visual inspection
Compare images with labelled examples of acceptable and unusual states.
Document capture
Extract image regions and text for downstream validation.
Beyond a bounding box.
- Annotation specification
- Vision inference pipeline
- Condition-based evaluation report
- Review interface and event integration
- Annotation specification
- Vision inference pipeline
- Condition-based evaluation report
- Review interface and event integration
Check a package label
Capability Example
A camera image shows a parcel with a potentially damaged barcode label.
Illustrative data. Nothing is sent to an external system.
Example workspace1 / 3
Captured frame
- Camera: line-02
- Object: parcel label
- Glare detected across barcode
Detect the label region and check whether the image is usable.
Barcode check
- Label region cropped
- Barcode partly unreadable
- Shipment match: not confirmed
Decode the barcode and compare it with the expected shipment.
Operator queue
- Review required
- Image and region attached
- No automatic rejection
Send unreadable or mismatched labels to an operator.
A matched shipment ID or a review item carrying the source crop and reason.
Follow the record through the next step
Start with the real camera conditions.
Capture conditions
Evaluate lighting, movement, occlusion and camera placement using representative images.
Retention
Define image storage, access and deletion rules before collecting production footage.
Questions before we start.
Can you use our existing cameras?
We first check resolution, frame access, placement and network constraints. Hardware replacement is a separate decision.
How is accuracy assessed?
Use task-specific measures such as precision and recall, broken down by operating condition and the cost of missed or false detections.
Bring your starting point.
Share representative images and the actions a correct or uncertain detection should trigger.
