Urban Remote Sensing Scene Recognition Based on Lightweight Convolution Neural Network

Xia, Jingming; Ding, Yue; Tan, Ling

doi:10.1109/access.2021.3057868

Cited by 9 publications

(5 citation statements)

References 32 publications

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“…In the intelligent driving perception module, the camera is the essential key hardware. The cameras used mainly include a monocular camera and binocular camera [8]. Vorugunti and others believe that the vehicle camera based on machine vision can recognize a variety of objects in the driving environment of intelligent vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Control of a Driverless Energy Vehicle Based on an Environment Sensing Sensor

Xue

Lou

2022

Wireless Communications and Mobile Computing

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In order to solve the complex environment in the process of vehicle driving and the complexity of self-vehicle structure, intelligent vehicles are prone to rear end collision, lateral collision, and other safety accidents in the presence of tall trees, mountains, and other road environments, endangering the safety of people on board. According to parameters such as the speed of the vehicle, the movement of the blind spot, and the relationship between the vehicle and the blind spot, the model is based on the safety mode of the preceding vehicle. Based on the static obstacles that may exist in the sensing blind area, a sensor sensing blind area safety distance model is established. Based on the possible dynamic obstacles, the active collision avoidance algorithm based on the sensor perceived blind area is studied and simulated. The experimental results show that the selected sensor sensing blind area active collision avoidance controller can well adapt to a variety of special and emergency working conditions, can accurately complete the accurate control of sensor sensing blind area active collision avoidance, and avoid collision accidents to the greatest extent. Compared with the control group, the system designed in this paper can avoid more than 80% of the collision scenes compared with the previous anticollision system. It provides a reference for the future research of sensor sensing blind area-related topics and sensor sensing blind area active collision avoidance system. To a certain extent, it can improve the ability of intelligent vehicle environmental perception and reduce the incidence of rear end collision accidents.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent Control of a Driverless Energy Vehicle Based on an Environment Sensing Sensor

Xue

Lou

2022

Wireless Communications and Mobile Computing

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“…Additionally, learning local and global information in conjunction is primarily based on transformerbased networks [122,124,127,143,144], while the utilization of the dual-branch network is explored in [145]. Meanwhile, lightweight CNNs are used in [10,96] and DenseNet in [95]. For reducing the effect of high intra-class diversity and high inter-class similarity in the dataset, metric branches [106] and re-organized classes in a two-layer hierarchy [71] are implemented.…”

Section: Research Problem and Utilized Research Techniquesmentioning

confidence: 99%

“…Fine-tuned CNNs [86,89] involved modifications to hyper-parameters to optimize performance, while LBP-based CNNs [46,83,84] utilized LBP mapped images to capture texture information for scene classification. Lightweight CNNs [10,96] prioritize parameter reduction for faster performance. These methods, however, often face challenges in effectively classifying complex scenes due to their reliance on processing the entire image as a whole.…”

Section: Shift Of Paradigm In Scene Classification Architecturesmentioning

confidence: 99%

“…[2], drawing considerable attention. In recent years, the application of scene classification in VHR satellite images is evident in disaster detection [3], land use [4][5][6][7][8][9], and urban planning [10]. The implementation of deep learning (DL) for scene classification is an emerging tendency in a current scenario, with an effort to achieve maximum accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis

Thapa,

Horanont,

Neupane

et al. 2023

Remote Sensing

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Remote sensing image scene classification with deep learning (DL) is a rapidly growing field that has gained significant attention in the past few years. While previous review papers in this domain have been confined to 2020, an up-to-date review to show the progression of research extending into the present phase is lacking. In this review, we explore the recent articles, providing a thorough classification of approaches into three main categories: Convolutional Neural Network (CNN)-based, Vision Transformer (ViT)-based, and Generative Adversarial Network (GAN)-based architectures. Notably, within the CNN-based category, we further refine the classification based on specific methodologies and techniques employed. In addition, a novel and rigorous meta-analysis is performed to synthesize and analyze the findings from 50 peer-reviewed journal articles to provide valuable insights in this domain, surpassing the scope of existing review articles. Our meta-analysis shows that the most adopted remote sensing scene datasets are AID (41 articles) and NWPU-RESISC45 (40). A notable paradigm shift is seen towards the use of transformer-based models (6) starting from 2021. Furthermore, we critically discuss the findings from the review and meta-analysis, identifying challenges and future opportunities for improvement in this domain. Our up-to-date study serves as an invaluable resource for researchers seeking to contribute to this growing area of research.

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“…Xia et al, 31 (2021), a novel lightweight method dependent upon VGG16 was presented for extracting various features of RSI by 5 convolutional elements. This method utilizes depthwise separable convolutional for reducing the network limitations.…”

Section: Related Workmentioning

confidence: 99%

White shark optimizer with optimal deep learning based effective unmanned aerial vehicles communication and scene classification

Nadana Ravishankar,

Ramprasath,

Daniel

et al. 2023

Sci Rep

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Unmanned aerial vehicles (UAVs) become a promising enabler for the next generation of wireless networks with the tremendous growth in electronics and communications. The application of UAV communications comprises messages relying on coverage extension for transmission networks after disasters, Internet of Things (IoT) devices, and dispatching distress messages from the device positioned within the coverage hole to the emergency centre. But there are some problems in enhancing UAV clustering and scene classification using deep learning approaches for enhancing performance. This article presents a new White Shark Optimizer with Optimal Deep Learning based Effective Unmanned Aerial Vehicles Communication and Scene Classification (WSOODL-UAVCSC) technique. UAV clustering and scene categorization present many deep learning challenges in disaster management: scene understanding complexity, data variability and abundance, visual data feature extraction, nonlinear and high-dimensional data, adaptability and generalization, real-time decision making, UAV clustering optimization, sparse and incomplete data. the need to handle complex, high-dimensional data, adapt to changing environments, and make quick, correct decisions in critical situations drives deep learning in UAV clustering and scene categorization. The purpose of the WSOODL-UAVCSC technique is to cluster the UAVs for effective communication and scene classification. The WSO algorithm is utilized for the optimization of the UAV clustering process and enables to accomplish effective communication and interaction in the network. With dynamic adjustment of the clustering, the WSO algorithm improves the performance and robustness of the UAV system. For the scene classification process, the WSOODL-UAVCSC technique involves capsule network (CapsNet) feature extraction, marine predators algorithm (MPA) based hyperparameter tuning, and echo state network (ESN) classification. A wide-ranging simulation analysis was conducted to validate the enriched performance of the WSOODL-UAVCSC approach. Extensive result analysis pointed out the enhanced performance of the WSOODL-UAVCSC method over other existing techniques. The WSOODL-UAVCSC method achieved an accuracy of 99.12%, precision of 97.45%, recall of 98.90%, and F1-score of 98.10% when compared to other existing techniques.

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Urban Remote Sensing Scene Recognition Based on Lightweight Convolution Neural Network

Cited by 9 publications

References 32 publications

Intelligent Control of a Driverless Energy Vehicle Based on an Environment Sensing Sensor

Intelligent Control of a Driverless Energy Vehicle Based on an Environment Sensing Sensor

Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis

White shark optimizer with optimal deep learning based effective unmanned aerial vehicles communication and scene classification

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