A Lightweight Detection Method for Remote Sensing Images and Its Energy-Efficient Accelerator on Edge Devices.

Convolutional neural networks (CNNs) have been extensively employed in remote sensing image detection and have exhibited impressive performance over the past few years. However, the abovementioned networks are generally limited by their complex structures, which make them difficult to deploy with power-sensitive and resource-constrained remote sensing edge devices. To tackle this problem, this study proposes a lightweight remote sensing detection network suitable for edge devices and an energy-efficient CNN accelerator based on field-programmable gate arrays (FPGAs).