Object Detection
What is Object Detection?
Object detection is an AI technique that identifies and locates multiple objects within an image or video in real time. In the context of Edge AI, it enables devices like cameras, drones, and IoT sensors to analyze visual data locally, reducing latency and dependence on the cloud.
Why Is It Used?
It’s used to empower machines to understand their surroundings — detecting people, vehicles, or products in industrial, retail, or smart city environments. Edge-based object detection allows faster decisions, greater privacy, and offline intelligence, making it ideal for mission-critical applications where milliseconds matter.
How Is It Used?
At the edge, object detection models are deployed directly on devices using optimized neural networks. They process video feeds in real time for tasks such as:
Monitoring factory floors for safety compliance
Tracking vehicle movement in traffic systems
Enabling autonomous drones to avoid obstacles
Powering retail analytics through smart cameras
Types of Object Detection
Single Shot Detectors (SSD): Fast and efficient for real-time edge use.
You Only Look Once (YOLO): High-speed detection with minimal compute.
Region-Based CNN (R-CNN): High accuracy, used where precision outweighs speed.
Transformer-based Models: Advanced models like DETR for context-aware vision tasks.
Benefits of Object Detection
Low Latency: Immediate insights without cloud dependency.
Data Privacy: Processing stays on-device, reducing data transmission risks.
Cost Efficiency: Less bandwidth and cloud storage usage.
Scalability: Easily deployable across distributed IoT ecosystems.
Energy Efficiency: Optimized inference on low-power edge devices.