Intelligent hyperspectral target detection for reliable IoV applications

Abstract

In recent years, hyperspectral imagery has played a significant role in IoV (Internet of Vehicles) vision areas such as target acquisition. Researchers are focusing on integrating detection sensors, detection computing units, and communication units into vehicles to expand the scope of target detection technology with hyperspectral imagery. As imaging spectroscopy technology gradually matures, the spectral resolution of captured hyperspectral images is increasing. At the same time, the volume of data is also increasing. As a result, the reliability of IoV applications is challenged. In this paper, an intelligent hyperspectral target detection method based on deep learning network is proposed. It is based on the residual network structure with the addition of an attention mechanism. The trained network model requires few computational resources and can provide the results in a short time. Our method improves the value of mAP50 by an average of 3.57% for all categories and by up to 5% for a single category on the public dataset.