Advanced Computer Vision

This course delves deeper into the world of Computer Vision (CV), exploring advanced techniques and applications that are transforming Supply Chain Management (SCM). You'll gain insights into cutting-edge approaches for object detection, image segmentation, and video analysis, empowering you to unlock the full potential of visual data in your supply chains.

• Explore advanced object detection techniques like You Only Look Once (YOLO) and explore their advantages for real-time applications in SCM.

• Understand semantic segmentation approaches like U-Net for detailed image analysis tasks within warehouses and logistics.

• Gain insights into video analysis techniques like object tracking and anomaly detection for automated surveillance and process monitoring.

• Identify advanced applications of CV in SCM (e.g., automated robotic picking and grasping, defect detection in high-resolution images).

• Explore the integration of CV with other AI techniques for a holistic approach to SCM optimization.

1. Advanced Object Detection

• Beyond bounding boxes: Exploring YOLO (You Only Look Once) for real-time object detection.

• Understanding the YOLO architecture and its advantages for fast and accurate object localization.

• Hands-on Coding Exercises: Implementing a basic YOLO model for object detection in SCM images (using libraries like TensorFlow or PyTorch).

• Case Studies: Exploring YOLO applications in real-time object detection for automated inventory counting or package sorting in warehouses.

• Deep dive into advanced object detection techniques like Single-Shot MultiBox Detector (SSD) (optional).

2. Image Segmentation for In-depth Analysis

• Introduction to Semantic Segmentation: Going beyond classification to pixel-level understanding.

• Understanding U-Net architecture and its effectiveness for detailed image segmentation tasks.

• Hands-on Coding Exercises: Utilizing U-Net for segmenting objects or regions of interest in SCM images (e.g., identifying damaged products or misplaced items).

• Case Studies: Exploring applications of image segmentation in quality control, identifying anomalies in product packaging, or analyzing warehouse layouts.

• Introduction to other segmentation techniques like Mask R-CNN (optional).

3. Video Analysis and Beyond

• Exploring video analysis techniques: Object tracking for monitoring movement of goods and anomaly detection for identifying unusual events.

• Understanding how object tracking algorithms follow objects across video frames.

• Hands-on Exercises: Implementing basic object tracking algorithms for analyzing movement patterns in SCM videos.

• Case Studies: Exploring applications of video analysis in automated surveillance of transportation routes, monitoring loading/unloading activities, and detecting safety hazards.

• Integration of CV with other AI techniques: Combining CV with Reinforcement Learning for robotic manipulation tasks in warehouses (optional).

• Strong understanding of linear algebra, calculus, and probability theory

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with basic computer vision concepts and techniques (e.g., image processing, feature extraction)

• Knowledge of convolutional neural networks (CNNs) and their applications