Many companies have dozens of cameras recording around the clock, yet nobody looks at the footage unless something happens. On production lines, someone often has to watch products for hours to pick out defective ones, a tiring job whose accuracy drops as time goes on. Computer vision is a branch of AI that lets software “see” images and video and make decisions about them.
What can computer vision do?
- Image classification: Telling whether something is intact or defective, full or empty.
- Object detection: Finding and counting people, vehicles, boxes or tools in an image.
- Text reading (OCR): Extracting text from invoices, forms, scanned documents or license plates.
- Event detection: Entry into a restricted area, a package left in one spot, or a missing safety helmet.
- Face recognition: Identifying people by their faces, the most powerful and also the most sensitive application.
Common business applications
- Quality control: A camera above the production line that detects scratches, wrong colors, crooked labels or missing parts.
- Parking and access: Reading license plates and logging vehicle entry and exit times.
- Site security: Alerts on movement after hours or fence crossings, instead of reviewing hours of footage.
- Warehouses and stores: Counting stock, detecting empty shelves and analyzing visitor traffic.
- Documents and invoices: Automatically reading invoices and forms and entering the data into accounting software, with human review.
Benefits
- Repetitive visual work is done continuously and without fatigue.
- Response to events is faster, because alerts arrive in real time.
- You get measurable data from things that used to be only seen, such as defect rates or peak hours.
Limitations and risks
- Dependence on image conditions: Low light, reflections, poor angles, dust or camera vibration can sharply reduce accuracy. A system that worked well in testing may perform poorly on the night shift.
- False positives and negatives: Too many false alerts lead operators to ignore alerts; missed cases in quality control also have a cost.
- Need for training data: For your specific product or defect, a large number of images usually has to be collected and labeled.
- Privacy: Recording and analyzing images of employees and customers, especially with face recognition, is a sensitive matter. Keep the purpose narrow and clear, inform people, define how long images are kept, and consult the company’s legal advisor before deployment.
Before buying any system, ask the vendor to test it on real images from your own environment, at different times and lighting conditions, not on demo samples.
The infrastructure you need
Computer vision is, above all, a network and storage project. Processing can happen on the camera itself or a device next to it (Edge), on an on-premises server with a GPU, or in a cloud service; each option has different cost, latency and data risk. Streaming video from many cameras uses a lot of bandwidth, so put cameras on a separate VLAN, change their default passwords, keep firmware up to date, and back up the image storage and system settings.
Checklist
- The problem and the success metric, such as defect detection rate, are defined.
- Testing has been done on real images from your environment.
- Camera lighting, angles and quality have been checked.
- The processing location (Edge, on-premises server or cloud) has been chosen.
- Cameras are on a separate network with unique passwords.
- A privacy policy and image retention period are written down.
Computer vision projects depend on a stable network, suitable servers and secure storage. ITPlus724 engineers can design and implement the camera network, servers and backup your company needs.