Vision-Based Novelty Detection Using Deep Features and Evolved Novelty Filters for Specific Robotic Exploration and Inspection Tasks

Author:

Contreras-Cruz Marco AntonioORCID,Ramirez-Paredes Juan PabloORCID,Hernandez-Belmonte Uriel HaileORCID,Ayala-Ramirez VictorORCID

Abstract

One of the essential abilities in animals is to detect novelties within their environment. From the computational point of view, novelty detection consists of finding data that are different in some aspect to the known data. In robotics, researchers have incorporated novelty modules in robots to develop automatic exploration and inspection tasks. The visual sensor is one of the preferred sensors to perform this task. However, there exist problems as illumination changes, occlusion, and scale, among others. Besides, novelty detectors vary their performance depending on the specific application scenario. In this work, we propose a visual novelty detection framework for specific exploration and inspection tasks based on evolved novelty detectors. The system uses deep features to represent the visual information captured by the robots and applies a global optimization technique to design novelty detectors for specific robotics applications. We verified the performance of the proposed system against well-established state-of-the-art methods in a challenging scenario. This scenario was an outdoor environment covering typical problems in computer vision such as illumination changes, occlusion, and geometric transformations. The proposed framework presented high-novelty detection accuracy with competitive or even better results than the baseline methods.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. AutoExplorers: Autoencoder-Based Strategies for High-Entropy Exploration in Unknown Environments for Mobile Robots;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Learning citywide patterns of life from trajectory monitoring;Proceedings of the 30th International Conference on Advances in Geographic Information Systems;2022-11

3. Domain Invariant Siamese Attention Mask for Small Object Change Detection via Everyday Indoor Robot Navigation;2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2022-10-23

4. Fusion of Novelty Detectors Using Deep and Local Invariant Visual Features for Inspection Task;IEEE Access;2022

5. Real-Time On-Board Deep Learning Fault Detection for Autonomous UAV Inspections;Electronics;2021-05-05

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3