A 3D Object Detection Based on Multi-Modality Sensors of USV

Yingying Wu,Hao Liu,Zhiqiang Wei,Huacheng Qin,Tao Liu

doi:10.3390/app9030535

Abstract

Unmanned Surface Vehicles (USVs) are commonly equipped with multi-modality sensors. Fully utilized sensors could improve object detection of USVs. This could further contribute to better autonomous navigation. The purpose of this paper is to solve the problems of 3D object detection of USVs in complicated marine environment. We propose a 3D object detection Depth Neural Network based on multi-modality data of USVs. This model includes a modified Proposal Generation Network and Deep Fusion Detection Network. The Proposal Generation Network improves feature extraction. Meanwhile, the Deep Fusion Detection Network enhances the fusion performance and can achieve more accurate results of object detection. The model was tested on both the KITTI 3D object detection dataset (A project of Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago) and a self-collected offshore dataset. The model shows excellent performance in a small memory condition. The results further prove that the method based on deep learning can give good accuracy in conditions of complicated surface in marine environment.

Highlights

In the new era of ocean observations, Unmanned Surface Vehicles (USVs) are of vital significance in scientific investigation, ocean monitoring and disaster relief [1]
This paper proposes a 3D object detection Depth Neural Network (DNN) based on multi-modality data of USVs
Using the deep fusion network, the depth expanding of the new network in this paper introduces more parameters that increase expression ability

Summary

Introduction

In the new era of ocean observations, Unmanned Surface Vehicles (USVs) are of vital significance in scientific investigation, ocean monitoring and disaster relief [1]. Most applications of USVs, such as collision avoidance and navigation, heavily rely on manual operation. Achievement of reliable, autonomous, all-weather marine object detection and characterization can be highly beneficial if the capability for autonomous collision avoidance of USVs could be realized. Other advanced tasks such as port surveillance require semantic reconstruction of the environment. Accurate object detection could significantly improve the performance of autonomous navigation and related advanced tasks. Existing methods of object detection face some challenges in USVs’

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Conclusion