// Discipline 05

See what machines see in production.

Object detection, segmentation, tracking, OCR. End-to-end computer vision pipelines from camera to decision — built for real-world conditions, not benchmark datasets.

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/ 5.1

Object Detection & Classification

Real-time detection of objects, faces, and anomalies — optimized for speed, accuracy, and edge deployment.

// YOLOv8 · batch_size: 8 · inference: 14ms

// Details

YOLO (v8, v9, v11), RT-DETR, Faster R-CNN
Custom class training with minimal data
TensorRT / ONNX / CoreML optimization
Batch inference and video stream processing

// Output formats

COCO JSONYOLO TXTONNXTensorRT

/ 5.2

Semantic & Instance Segmentation

Per-pixel class maps and instance masks for dense scene understanding — medical imaging, defect inspection, agriculture.

// instance_seg · 3 instances · mIoU: 0.89

// Details

Mask R-CNN, SAM, Mask2Former
Real-time segmentation with YOLOv8-seg
Multi-class and panoptic segmentation
Interactive refinement with SAM integration

// Output formats

COCO RLEPNG masksPolygon JSON

/ 5.3

Multi-Object Tracking (MOT)

Track objects across video frames, occlusions, and camera cuts — with re-identification and trajectory prediction.

// ByteTrack · 5 active tracks · frame: 482

// Details

ByteTrack, BoT-SORT, DeepSORT
Re-ID for cross-camera tracking
Trajectory smoothing and prediction
Track association with Kalman filtering

// Output formats

MOT17/20 formatTrack JSONCSV

/ 5.4

OCR & Document Analysis

Text detection and recognition from images, PDFs, and scanned documents — with layout analysis and post-correction.

// ocr_output · tesseract · conf_threshold: 0.7

INVOICE #2024-3847^0.94

Date: May 24, 2026^0.97

Total: $4,820.00^0.99

Vendor: Acme Corp Ltd.^0.92

extracted: 4 fields · layout: form · lang: eng · postprocess: ✓

// Details

Tesseract, PaddleOCR, EasyOCR
Document layout analysis (tables, forms)
Multi-language support (100+ languages)
Post-processing with language models

// Output formats

JSONhOCRALTO XMLPlain text

/ 5.5

Edge & Cloud Deployment

Deploy vision models on edge devices (Jetson, Coral), cloud (Triton, SageMaker), or mobile (iOS, Android).

// deployment · yolov8_optimized · production

platform ............ NVIDIA Jetson AGX Orin

backend ............ TensorRT · FP16

model_size ............ 26 MB vs 52 MB FP32

inference ............ 12ms 83 FPS

power ............ 18W edge optimized

mAP@0.5 ............ 0.91 –2.1% vs FP32

// Details

TensorRT for NVIDIA GPUs
ONNX Runtime for CPU inference
CoreML for iOS, TFLite for Android
Model quantization (INT8, FP16)

// Output formats

DockerONNXTensorRTMobile SDKs

/ 5.6

Video Analytics & Insights

Transform raw video streams into structured insights — people counting, anomaly detection, behavior analysis.

// video_analytics · event_log · store_432

people_count ......... 18 current

occupancy ......... 45% capacity: 40

dwell_time_avg ......... 4m 32s

hotspot_zone ......... entrance 62% traffic

anomaly ......... none last 4h

alert: occupancy → 92% at 14:22 UTC

// Details

Crowd counting and density estimation
Anomaly detection in surveillance
Action recognition (fall detection, intrusion)
Real-time alerting and event triggers

// Output formats

JSON eventsREST APIWebSocket

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