A Fast Target Detection Model for Remote Sensing Images Leveraging Roofline Analysis on Edge Computing Devices

Abstract

Deploying image target detection algorithms on embedded devices is critical. Previous studies assumed that fewer model parameters and computations improved the inference speed. However, many models with few parameters and computations have slow inference speeds. Therefore, developing a remote sensing image target detection model that can perform real-time inference on embedded devices is required. We propose a fast target detection model for remote sensing images leveraging roofline analysis on edge computing devices (FTD-RLE). It comprises three parts: (1) We analyze the hardware characteristics of embedded devices using RoofLine and incorporate global features to design a model structure based on the operational intensity (OI) and arithmetic intensity (AI) of embedded devices. (2) The mirror ring convolution (MRC) is designed for extracting global features. The global information-aware module (GIAM) extracts local features from key areas using the global feature guidance model. The global-local feature pyramid module (GLFPM) is proposed to combine global and local features. (3) Additionally, hardware deployment and inference acceleration technologies are implemented to enable the model's deployment on edge devices. TensorRT and quantization methods are used to ensure fast inference speed. The proposed algorithm achieves an average detection accuracy of 92.3% on the VHR-10 dataset and 95.2% on the RSOD dataset. It has 1.26 M model parameters, and the inference time for processing one image on Jetson Orin Nx is 8.43ms, which is 1.90 ms and 1.98 ms faster than the mainstream lightweight algorithms ShufflenetV2 and GhostNet, respectively.