{"title":"Feature refinement with DBO: optimizing RFRC method for autonomous vehicle detection","authors":"","doi":"10.1007/s11370-024-00520-x","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>In today’s world, the utilization of a large number of vehicles has led to congested traffic conditions and an increase in accidents. These issues are considered primary problems in the transportation field. Therefore, there is a pressing need to develop a novel method for monitoring traffic. To address this, we propose a new model called the residual faster recurrent convolutional (RFRC) algorithm. While the proposed model achieves good detection accuracy, it must also meet the demands of real-life scenarios. In this approach, the ResNet-50 model is combined with the faster recurrent-based convolutional neural network (FRCNN) to enable the detection of autonomous vehicles. We utilize the dung beetle optimizer (DBO) with a crossover strategy for feature selection, focusing on selecting relevant features for analysis. To validate the effectiveness of the proposed RFRC method, we conduct experiments using two datasets: the KITTI dataset and the COCO2017 dataset. The evaluation of the RFRC model is performed using various measures, including f1-score, precision, recall, accuracy, and specificity, on both datasets. The proposed RFRC model outperforms both datasets and attains better results in autonomous vehicle detection.</p>","PeriodicalId":48813,"journal":{"name":"Intelligent Service Robotics","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Service Robotics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11370-024-00520-x","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
Abstract
In today’s world, the utilization of a large number of vehicles has led to congested traffic conditions and an increase in accidents. These issues are considered primary problems in the transportation field. Therefore, there is a pressing need to develop a novel method for monitoring traffic. To address this, we propose a new model called the residual faster recurrent convolutional (RFRC) algorithm. While the proposed model achieves good detection accuracy, it must also meet the demands of real-life scenarios. In this approach, the ResNet-50 model is combined with the faster recurrent-based convolutional neural network (FRCNN) to enable the detection of autonomous vehicles. We utilize the dung beetle optimizer (DBO) with a crossover strategy for feature selection, focusing on selecting relevant features for analysis. To validate the effectiveness of the proposed RFRC method, we conduct experiments using two datasets: the KITTI dataset and the COCO2017 dataset. The evaluation of the RFRC model is performed using various measures, including f1-score, precision, recall, accuracy, and specificity, on both datasets. The proposed RFRC model outperforms both datasets and attains better results in autonomous vehicle detection.
期刊介绍：
The journal directs special attention to the emerging significance of integrating robotics with information technology and cognitive science (such as ubiquitous and adaptive computing,information integration in a distributed environment, and cognitive modelling for human-robot interaction), which spurs innovation toward a new multi-dimensional robotic service to humans. The journal intends to capture and archive this emerging yet significant advancement in the field of intelligent service robotics. The journal will publish original papers of innovative ideas and concepts, new discoveries and improvements, as well as novel applications and business models which are related to the field of intelligent service robotics described above and are proven to be of high quality. The areas that the Journal will cover include, but are not limited to: Intelligent robots serving humans in daily life or in a hazardous environment, such as home or personal service robots, entertainment robots, education robots, medical robots, healthcare and rehabilitation robots, and rescue robots (Service Robotics); Intelligent robotic functions in the form of embedded systems for applications to, for example, intelligent space, intelligent vehicles and transportation systems, intelligent manufacturing systems, and intelligent medical facilities (Embedded Robotics); The integration of robotics with network technologies, generating such services and solutions as distributed robots, distance robotic education-aides, and virtual laboratories or museums (Networked Robotics).
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