What is Image Processing? How Does It Work and Where is It Used in Industry?What is image processing and how does it work? Steps of image processing from classical algorithms to artificial intelligence, industrial applications, hardware requirements, and example projects.

A digital image is a matrix composed of pixels, each carrying colour and brightness values. The image processing system works on this matrix step by step:

• Image acquisition: IP camera, industrial camera, or smartphone captures the image. IP cameras supporting RTSP/ONVIF are commonly used in factories.
• Preprocessing: Noise reduction, contrast enhancement, perspective correction, and resizing are performed.
• Feature extraction: Edges, colour regions, textures, or representations learned by the network in deep learning are extracted.
• Analysis and decision: Object detection (is there a helmet?), classification (is the part defective?), segmentation (what is the area of the stain?) or tracking (where is the forklift going?) are carried out.
• Action: The result is sent back to the field as an alarm, report, PLC signal, turnstile lock, or ERP record.

Classical image processing relies on rules written manually by engineers: “if the number of pixels within this colour range exceeds the threshold, count as a defect”. It is still very effective on lines with fixed lighting, fixed angles, and uniform products. However, rules break down as the environment changes.

Deep learning-based image processing learns its own rules from thousands of sample images. It can generalise despite changes in lighting, angle, clothing colour, or product variation. Therefore, it has become the standard for “rule-defying” problems such as occupational safety, human behaviour, and variable surface defects. For a detailed comparison, please see our machine vision and deep learning article.

1. Quality Control and Defect Detection

Scratches, stains, cracks, missing assemblies, incorrect labels or colour deviations are detected at production speed. Defective products are separated before reaching the customer, reducing scrap rates and return costs. See quality control with image processing and surface defect detection.

2. Occupational Health and Safety (OHS)

Deficiencies in PPE such as helmets, vests, goggles, and gloves; entry into restricted areas, forklift-pedestrian proximity, and fall incidents are monitored 24/7. Every frame is checked in the field where human supervision is only sampled. See helmet detection system, PPE control, forklift-pedestrian safety.

3. Productivity and OEE

Line stops, cycle times, station occupancy, and idle times are automatically measured from the camera to provide real-time dashboards instead of end-of-shift Excel sheets. See OEE tracking system and workforce productivity analysis.

4. Logistics, Counting and Traceability

Counting pallets, boxes, vehicles; ramp occupancy and shipment verification are performed. Counting data is transferred to WMS/ERP systems via API. See warehouse and logistics safety.

For many applications, buying new cameras is unnecessary. Existing IP cameras at the facility (mostly 1080p, 15–25 FPS) are sufficient for OHS and productivity analysis. For quality control stations searching for micron-level defects, industrial cameras and controlled lighting may be required. Analysis can be performed on an edge device within the facility (without sending images outside) or in the cloud. Details: Are existing IP cameras sufficient for artificial intelligence?

Let’s discover together in the initial meeting which problem image processing can solve in your facility.

• Discovery: Problem, target metric and camera positions are determined together.
• Data and model: Sample images are collected from the field; a pre-trained model is calibrated or a custom model is trained.
• Pilot: A few weeks of trial measurements are carried out on 1–2 lines or areas.
• Deployment: Alarm rules, reports, and ERP/MES integration extend the solution to the entire facility.