Object Detection

VisionLog includes a YOLO-based object detection pipeline for detecting and classifying objects in real-time video streams.

Architecture Overview

Object Detection Flow

The object detection system consists of two main phases:

Step	Component	Description
1	Object Images	Reference images organized by class
2	Enrollment	Extract embeddings from reference images
3	Object Database	Store class-to-embedding mappings

Step	Component	Description
1	Input	Camera or video feed
2	Detection	Locate objects using YOLO
3	Object Match	Compare against database embeddings
4	Object Logging	Record detection with metadata
5	Detection Log	Output: date, time, class, confidence

Component	Technology	Purpose
Detection	YOLO	Real-time object detection
Framework	PyTorch	Deep learning inference
Model API	Ultralytics	YOLO model interface
Image Processing	OpenCV	Frame capture and processing
Language	Python	Core implementation

Explore each stage of the object detection pipeline:

Stage	Description
Enrollment	Register object classes and extract embeddings
Detection	YOLO-based object localization
Matching	Compare detections against database
Logging	Record and export detection events

Application	Description
Inventory Tracking	Monitor objects entering/leaving areas
Safety Monitoring	Detect PPE, hazards, or restricted items
Asset Management	Track equipment and tools
Custom Detection	Any trainable object category