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Lightweight Network Ensemble Architecture for Environmental Perception on the Autonomous System

文献类型： 外文期刊

作者： Dai, Yingpeng 1 ; Wang, Junzheng 1 ; Li, Jing 1 ; Meng, Lingfeng 2 ; Wang, Songfeng 2 ;

作者机构： 1.Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China

2.Inst Tobacco Res CAAS, Qingdao 266000, Peoples R China

关键词： Neural network ensemble; real-time application; classification; semantic segmentation

期刊名称：CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES （ 影响因子：2.027； 五年影响因子：1.764 ）

ISSN： 1526-1492

年卷期： 2023 年 134 卷 1 期

页码：

收录情况： SCI

摘要： It is important for the autonomous system to understand environmental information. For the autonomous system, it is desirable to have a strong generalization ability to deal with different complex environmental information, as well as have high accuracy and quick inference speed. Network ensemble architecture is a good choice to improve network performance. However, it is unsuitable for real-time applications on the autonomous system. To tackle this problem, a new neural network ensemble named partial-shared ensemble network (PSENet) is presented. PSENet changes network ensemble architecture from parallel architecture to scatter architecture and merges multiple component networks together to accelerate the inference speed. To make component networks independent of each other, a training method is designed to train the network ensemble architecture. Experiments on Camvid and CIFAR-10 reveal that PSENet achieves quick inference speed while maintaining the ability of ensemble learning. In the real world, PSENet is deployed on the unmanned system and deals with vision tasks such as semantic segmentation and environmental prediction in different fields.

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