Context (archaeology)

Edge detection

World Wide Web

Linguistics

Process (computing)

Engineering

Philosophy

Object detection

Biology

Image processing

Systems engineering

Server

Operating system

Computer science

Cloud computing

Computer network

Paleontology

Physics

Task (project management)

Pattern recognition (psychology)

Politics

Optics

Image (mathematics)

Political science

Object (grammar)

Enhanced Data Rates for GSM Evolution

Low latency (capital markets)

Feature (linguistics)

Focus (optics)

Artificial intelligence

Representation (politics)

Edge enhancement

Edge computing

Computer vision

IEEE Transactions on Network Science and Engineering

With fast increase in volume of mobile multimedia data, how to apply powerful deep learning methods to process data with real-time response becomes a major issue. Meanwhile, edge computing structure helps improve response time and user experience by bringing flexible computation and storage capabilities. Considering both technologies for successful AI-based applications, we propose an edge-computing driven and end-to-end framework to perform tasks of image enhancement and object detection under low-light conditions. The framework consists of a cloud-based enhancement and an edge-based detection stage. In the first stage, we establish connections between edge devices and cloud servers to input re-scaled illumination parts of low-light images, where enhancement subnetworks are dynamically and parallel coupled to compute enhanced illumination parts based on low-light context. During the edge-based detection stage, edge devices could accurately and rapidly detect objects based on cloud-computed informative feature map. Experimental results show the proposed method significantly improves detection performance in low-light conditions with low latency running on edge devices.

Edge Computing Driven Low-Light Image Dynamic Enhancement for Object Detection