By the end of this course, learners will be able to:

1. Understand the evolution of computer vision and object detection models.
2. Explain key architectures including CNNs, R-CNNs, SSDs, and YOLO.
3. Preprocess, label, and augment image datasets for model training.
4. Train, validate, and tune object detection models using transfer learning.
5. Implement YOLO models for real-time detection and localization.
6. Evaluate model performance using IoU, mAP, and precision-recall metrics.
7. Optimize model inference with quantization and acceleration tools.
8. Integrate object detection models into real-time applications using OpenCV and Python.
9. Deploy trained models on cloud, web, or edge environments.
10. Build complete visual AI systems combining detection, tracking, and analytics.

Course Syllabus

Module 1: Introduction to Computer Vision and Deep Learning
Overview of computer vision, CNNs, and the evolution of object detection.

Module 2: Image Processing and Preprocessing Techniques
Image filtering, augmentation, normalization, and dataset preparation.

Module 3: Convolutional Neural Networks (CNNs)
Architecture, layers, filters, feature maps, and backpropagation fundamentals.

Module 4: Classical Object Detection Models
Overview of R-CNN, Fast R-CNN, and Faster R-CNN.

Module 5: Single-Shot Detectors (SSD, RetinaNet)
Design principles, anchor boxes, and performance trade-offs.

Module 6: YOLO – You Only Look Once
Deep dive into YOLOv3 to YOLOv8 architectures and training process.

Module 7: Custom Object Detection Projects
Building and labeling datasets using Roboflow or LabelImg; model training in PyTorch.

Module 8: Model Evaluation and Performance Metrics
Understanding IoU, precision, recall, F1 score, and mAP metrics.

Module 9: Real-Time Inference and Optimization
Deploying models with TensorRT, OpenVINO, and ONNX Runtime for performance optimization.

Module 10: Object Tracking and Segmentation
Implementing object tracking (SORT, DeepSORT) and semantic segmentation (Mask R-CNN).

Module 11: Deployment and Integration
Integrating models into applications using Flask, FastAPI, or Streamlit.

Module 12: Capstone Project – Building an Intelligent Detection System
Develop and deploy a real-time detection project such as vehicle tracking, defect detection, or surveillance monitoring.

Upon successful completion, learners will receive a Certificate of Mastery in Advanced Computer Vision (YOLO & Object Detection) from Uplatz.

This certification verifies your expertise in designing, training, and deploying state-of-the-art object detection systems using deep learning. It highlights your ability to apply YOLO architectures, manage datasets, and optimize real-time inference pipelines.

The certificate serves as proof of your technical capability to:

• Implement end-to-end object detection workflows from data to deployment.
• Integrate advanced vision models into real-world applications.
• Collaborate in AI projects requiring visual perception and automation expertise.

It demonstrates that you are qualified to work on AI, Robotics, and Computer Vision teams across industries like autonomous vehicles, smart surveillance, retail analytics, and healthcare imaging.

Proficiency in advanced computer vision unlocks lucrative roles in rapidly expanding fields such as AI, robotics, and automation. Common job titles include:

• Computer Vision Engineer
• AI/ML Engineer (Vision)
• Deep Learning Specialist
• Robotics Perception Engineer
• Research Scientist – Visual Intelligence
• AI Product Developer

Industries such as automotive, defense, healthcare, retail, and smart cities increasingly seek professionals with practical expertise in YOLO, TensorFlow, and PyTorch for vision-based AI applications.

1. What is object detection and how does it differ from image classification?
   Object detection identifies both what objects are present and where they are located in an image.
2. Explain the architecture of YOLO.
   YOLO divides the image into grids and predicts bounding boxes and class probabilities in a single pass, enabling real-time detection.
3. What metrics are used to evaluate object detection models?
   IoU (Intersection over Union), mAP (mean Average Precision), precision, recall, and F1 score.
4. What are the main differences between YOLO and Faster R-CNN?
   YOLO is faster and single-stage, while Faster R-CNN is more accurate but two-stage and computationally heavier.
5. What is transfer learning and why is it used in object detection?
   It reuses weights from pre-trained models to accelerate training and improve performance on smaller datasets.
6. What is Non-Maximum Suppression (NMS)?
   NMS removes overlapping bounding boxes to keep only the most confident predictions.
7. How do you handle class imbalance in object detection datasets?
   Through data augmentation, focal loss, or oversampling underrepresented classes.
8. What’s the advantage of YOLOv8 over earlier versions?
   It offers improved architecture, dynamic input scaling, and built-in segmentation and tracking capabilities.
9. What are the challenges in deploying real-time object detection?
   Latency, hardware constraints, model size, and balancing accuracy with speed.
10. Name real-world applications of YOLO-based systems.
    Autonomous driving, surveillance, retail analytics, medical imaging, and industrial defect detection.