iELMNet:集成新型改进的极限学习机和卷积神经网络模型用于交通标志检测。

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2023-10-01 Epub Date: 2022-01-06 DOI:10.1089/big.2021.0279
Aisha Batool, Muhammad Wasif Nisar, Jamal Hussain Shah, Muhammad Attique Khan, Ahmed A Abd El-Latif
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引用次数: 4

摘要

实时环境中的交通标志检测(TSD)对于自动驾驶车辆等应用具有重要意义。各种各样的交通标志、不同的外观和空间表现导致了巨大的类内变化。本文提出了一种基于极限学习机(ELM)、卷积神经网络(CNN)和尺度变换(ST)的模型,称为改进的极限学习机网络,用于实时环境中的交通标志检测。所提出的模型具有自定义的基于DenseNet的新型CNN架构、称为精确锚预测模型(A2PM)、ST和ELM模块的区域建议网络的改进版本。CNN架构利用手工制作的特征,如尺度不变特征变换和Gabor来即兴制作交通标志的边缘。A2PM使提取的特征之间的冗余最小化,以使模型高效,ST使模型能够检测不同大小的交通标志。ELM模块通过重塑功能来提高效率。该模型在三个公开的数据集上进行了测试,分别挑战了交通标志识别的真实和非真实环境、清华腾讯100K和德国交通标志检测基准,平均精度分别为93.31%、95.22%和99.45%。这些结果证明,所提出的模型比最先进的符号检测技术更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
iELMNet: Integrating Novel Improved Extreme Learning Machine and Convolutional Neural Network Model for Traffic Sign Detection.

Traffic sign detection (TSD) in real-time environment holds great importance for applications such as automated-driven vehicles. Large variety of traffic signs, different appearances, and spatial representations causes a huge intraclass variation. In this article, an extreme learning machine (ELM), convolutional neural network (CNN), and scale transformation (ST)-based model, called improved extreme learning machine network, are proposed to detect traffic signs in real-time environment. The proposed model has a custom DenseNet-based novel CNN architecture, improved version of region proposal networks called accurate anchor prediction model (A2PM), ST, and ELM module. CNN architecture makes use of handcrafted features such as scale-invariant feature transform and Gabor to improvise the edges of traffic signs. The A2PM minimizes the redundancy among extracted features to make the model efficient and ST enables the model to detect traffic signs of different sizes. ELM module enhances the efficiency by reshaping the features. The proposed model is tested on three publicly available data sets, challenging unreal and real environments for traffic sign recognition, Tsinghua-Tencent 100K, and German traffic sign detection benchmark and achieves average precisions of 93.31%, 95.22%, and 99.45%, respectively. These results prove that the proposed model is more efficient than state-of-the-art sign detection techniques.

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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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