Traffic Analysis Through Deep-Learning-Based Image Segmentation From UAV Streaming

Bisio, Igor; Garibotto, Chiara; Haleem, Halar; Lavagetto, Fabio; Sciarrone, Andrea

doi:10.1109/jiot.2022.3223283

Cited by 9 publications

(2 citation statements)

References 47 publications

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“…In recent years, attention mechanisms have gained considerable traction in the domain of road extraction. Extensive research has delved into self-attention, channel attention [ 64 ], spatial attention [ 65 , 66 ], and hybrid attention mechanisms [ 67 ]. The integration of the multi-head attention mechanism from Transformer [ 68 ] into architectures like ConSwin-Net [ 69 ] and Seg-Road [ 70 ] has effectively addressed the limitations of conventional CNNs, markedly enhancing the ability to perceive road texture intricacies and contextual information.…”

Section: Road Feature Extraction Based On Fully Supervised Deep Learn...mentioning

confidence: 99%

A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

Mo,

Shi,

Yuan

et al. 2024

Sensors

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Roads are the fundamental elements of transportation, connecting cities and rural areas, as well as people’s lives and work. They play a significant role in various areas such as map updates, economic development, tourism, and disaster management. The automatic extraction of road features from high-resolution remote sensing images has always been a hot and challenging topic in the field of remote sensing, and deep learning network models are widely used to extract roads from remote sensing images in recent years. In light of this, this paper systematically reviews and summarizes the deep-learning-based techniques for automatic road extraction from high-resolution remote sensing images. It reviews the application of deep learning network models in road extraction tasks and classifies these models into fully supervised learning, semi-supervised learning, and weakly supervised learning based on their use of labels. Finally, a summary and outlook of the current development of deep learning techniques in road extraction are provided.

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Section: Road Feature Extraction Based On Fully Supervised Deep Learn...mentioning

confidence: 99%

A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

Mo,

Shi,

Yuan

et al. 2024

Sensors

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“…The value of UAVs lies in their ability to form aerial platforms that can be used to extend the range of applications by integrating a variety of equipment. UAVs can replace human labor in high-altitude operations in various areas such as logistics delivery [1,2], agricultural plant protection [3,4], forest management [5,6], power patrols [7,8], displacement monitoring [9,10], disaster monitoring [11,12], disaster rescue [13,14], traffic analysis [15,16], railway inspections [17,18], and so on.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Observer and Adaptive Control for Disturbance Rejection of Quadrotor: A Survey

Wang,

Shen

2024

Actuators

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Quadrotors are widely applied in many fields, but they often face various external disturbances in actual operation. This makes it necessary to design a controller that can handle disturbances. Disturbance observer and adaptive control techniques are commonly used disturbance rejection techniques, the core idea of which is to estimate the disturbances in real time and incorporate the estimated values into the controller to suppress the disturbances. In this paper, various disturbance observers and adaptive control techniques, including nonlinear disturbance observers, extended state observers, neural networks, and fuzzy logic systems, are introduced, along with their variants or different structures. These techniques improve the adaptability and robustness of quadrotors to complex environments. Finally, future research directions for the disturbance rejection of quadrotors are also presented.

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CNNs for remote extraction of urban features: A survey-driven benchmarking

Neupane,

Aryal,

Rajabifard

2024

Expert Systems with Applications

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Traffic Analysis Through Deep-Learning-Based Image Segmentation From UAV Streaming

Cited by 9 publications

References 47 publications

A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

Disturbance Observer and Adaptive Control for Disturbance Rejection of Quadrotor: A Survey

CNNs for remote extraction of urban features: A survey-driven benchmarking

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