AI & Vision

Computer Vision & Object Detection (YOLO & PyTorch)

Computer Vision gives machines the ability to extract actionable insights from digital images and video feeds. Learn convolution kernels, real-time edge deployment, segmentation, and tracking algorithms.

Curated 2026 Curriculum GuideProject-Based Track

OpenCVYOLOv8 / YOLOv10PyTorchDeepSORTTensorRTAlbumentations

🇮🇳 Indian Market Benchmark

Expected CTC Range₹8.0L – ₹24.0L LPA

Estimated Timeline12 – 16 Weeks

Demand Scope5,500+ Openings

Experience LevelIntermediate to Advanced

Top Hubs:Bengaluru, Hyderabad, Chennai, Pune

Explore Career Compass Match

Core Track Highlights

Massive application in Indian smart cities, manufacturing quality inspection, and retail theft prevention

High demand in automotive autonomous ADAS systems (Tata Elxsi, Bosch India)

Combines image mathematics with real-time hardware execution

Structured Phase-by-Phase Syllabus

Focus on build-by-doing milestones rather than passive video consumption.

Weeks 1-4

Phase 1: Image Processing with OpenCV

Color spaces (RGB, HSV, Grayscale), thresholding, filtering
Edge detection (Sobel, Canny), contours, morphological ops
Data augmentation with Albumentations

🎯 Milestone Proof Project: Automated Factory Assembly Line Pill Defect Detector.

Weeks 5-8

Phase 2: Object Detection & Tracking with YOLO

YOLO architecture (backbone, neck, head) and anchor boxes
Annotation formats (COCO, Pascal VOC) and training custom weights
Multi-object tracking with DeepSORT and ByteTrack

🎯 Milestone Proof Project: Real-Time Indian Traffic Vehicle Counter & Speed Estimator.

Weeks 9-12

Phase 3: Segmentation & Edge Optimization

Semantic vs Instance segmentation with Mask R-CNN / YOLO-seg
Model quantization with ONNX and TensorRT acceleration
Deploying on Raspberry Pi / NVIDIA Jetson edge devices

🎯 Milestone Proof Project: Automated License Plate Recognition (ANPR) System.

Technical Interview Questions & Answers

Q1: What is IoU (Intersection over Union) in object detection?

IoU measures the overlap between the predicted bounding box and the ground truth bounding box: IoU = Area of Overlap / Area of Union. An IoU > 0.5 is commonly used to determine a true positive detection.