Lightweight Object Detection

Lightweight object detection focuses on developing efficient algorithms for identifying objects in images or videos, prioritizing speed and low resource consumption for deployment on resource-constrained devices like mobile phones and embedded systems. Current research emphasizes optimizing existing architectures such as YOLO and MobileNet, incorporating techniques like knowledge distillation, efficient convolution operations (e.g., Ghost convolution), and attention mechanisms to improve accuracy while minimizing computational cost and memory footprint. This field is crucial for enabling real-time object detection in applications ranging from autonomous vehicles and robotics to mobile augmented reality and low-power IoT devices.