AI

• Object detection and recognition using AI involves identifying and localizing multiple objects within an image using deep learning algorithms. The system processes visual data to both detect the location (bounding box) and classify the object type, even in complex or variable conditions such as different lighting, occlusions, or background clutter.
• The system ensures accurate identification of parts or assemblies in real time, enabling intelligent decision-making, robotic guidance, and flexible inspection.

Types of AI-Based Object Detection

Single-Stage Detection: Fast detection using unified model (e.g., YOLO, SSD)

Two-Stage Detection: High accuracy using regional proposals (e.g., Faster R-CNN)

Instance Segmentation: Pixel-level object segmentation (e.g., Mask R-CNN)

Key point Detection: Detect specific features or landmarks on an object

Types of AI-Based Object Detection

Typical Applications

• Real-time detection of multiple parts on a conveyor
• Robotic pick-and-place using detected object coordinates
• Tool presence verification in assembly fixtures
• Automated sorting of mixed part variants
• Component detection in bin-picking systems

Techniques

• Deep learning model training using labelled images of parts
• Real-time object detection with bounding box output
• Multi-class detection (identifying multiple objects in the same scene)
• Output integration for robotic automation or inspection logic
• Robust detection under rotation, scale, partial occlusion, and lighting variations

• Image classification using AI involves assigning a category or label to an entire image based on the visual content using deep learning models.
• The system learns from labeled datasets to recognize different parts, defects, or conditions within an image.

Types of Inspection

Binary Classification: Classifies an image into two categories (e.g., OK/NG)

Multi-Class Classification: Classifies an image into one of many possible classes

Hierarchical Classification: Classifies an image into nested groups or categories

Transfer Learning: Uses pre-trained networks to classify new image datasets

Typical Applications

• Part type classification in multi-model lines
• Defect vs non-defect classification
• Sorting of parts by visual appearance
• Pass/fail decision for quality inspection

Techniques

• CNN-based classification models (e.g., ResNet, MobileNet)
• Custom training using labelled images
• High-speed inference for real-time decision making
• Integration with sorting or rejection systems

• Image segmentation involves dividing an image into regions or objects by assigning each pixel a label. AI-based segmentation techniques are used for precise localization and identification of defects, parts, or boundaries in complex scenes.

Typical Applications

• Pixel-level defect localization
• Part shape extraction for measurement
• Semantic labelling in cluttered environments
• Surface anomaly detection on cast or forged components

Types of Inspection

Semantic Segmentation: Labels each pixel by class (e.g., background, object)

Instance Segmentation: Separates different instances of the same class

Edge-Based Segmentation: Detects precise object outlines using neural networks

Region Growing/Clustering: Groups similar pixel regions (classical + ML hybrid)

Techniques

• U-Net, DeepLab, Mask R-CNN-based models
• Segmentation masks for downstream decision logic
• AI-based learning from pixel-wise annotated datasets
• Output integration for dimensioning or region-specific analysis

• Image enhancement and restoration refer to improving the visual quality and interpretability of images by reducing noise, correcting distortions, or enhancing features. AI techniques enable intelligent enhancement under variable lighting, motion blur, or sensor artifacts.

Typical Applications

• Enhancing low-light or noisy images for inspection
• Restoring distorted or blurred images from moving conveyors
• Correcting uneven illumination across the image
• Improving image quality for OCR or defect detection

Types of Inspection

Noise Reduction: Removes random or periodic noise from images

Deblurring: Recovers image sharpness lost due to motion or focus error

Contrast Enhancement: Improves visibility of image details

Super-Resolution: AI-based upscaling to enhance the resolution of images

Techniques

• Deep learning-based filters (e.g., DnCNN, SRCNN)
• Adaptive contrast and brightness normalization
• Restoration of compressed or artifact-prone images
• Preprocessing stage for improved downstream performance