A Highway Traffic Image Enhancement Algorithm Based on Improved GAN in Complex Weather Conditions

Cheng, Xin; Zhou, Jingmei; Song, Jiachun; Zhao, Xiangmo

doi:10.1109/tits.2023.3258063

Cited by 8 publications

(2 citation statements)

References 38 publications

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“…The generator continually strives to increase this probability score. The eventual goal is to reach a state of Nash equilibrium [139], where the generator has effectively trained itself to generate data that the discriminator cannot reliably distinguish from actual data. In ITS, GANs are gaining prominence as powerful tools for generating synthetic data, addressing challenges related to traffic simulations, image recognition for autonomous vehicles, and predictive modeling for traffic flow [140], [141].…”

Section: Generative Adversarial Network (Gans)mentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

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Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather conditions. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues presents a significant endeavor. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects of explainability, transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.

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Section: Generative Adversarial Network (Gans)mentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…Son et al proposed to utilize the Stevens effect and local blur map to process the enhanced night road images by the cycle-consistent generative adversarial network to reduce the noise and enhance detail information 30 . Chen et al proposed an improved generative adversarial network to enhance the image quality of nighttime images and rain images by introducing the attention mechanism modules and the multiscale feature fusion modules into the generator network and local discrimination strategy into the discriminator 31 . Zhang et al designed a Decompose-Enhance-GAN Network tailored for augmenting low-light images 32 .…”

Section: Related Workmentioning

confidence: 99%

Nighttime road scene image enhancement based on cycle-consistent generative adversarial network

Jia,

Yu,

Chen

et al. 2024

Sci Rep

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During nighttime road scenes, images are often affected by contrast distortion, loss of detailed information, and a significant amount of noise. These factors can negatively impact the accuracy of segmentation and object detection in nighttime road scenes. A cycle-consistent generative adversarial network has been proposed to address this issue to improve the quality of nighttime road scene images. The network includes two generative networks with identical structures and two adversarial networks with identical structures. The generative network comprises an encoder network and a corresponding decoder network. A context feature extraction module is designed as the foundational element of the encoder-decoder network to capture more contextual semantic information with different receptive fields. A receptive field residual module is also designed to increase the receptive field in the encoder network.The illumination attention module is inserted between the encoder and decoder to transfer critical features extracted by the encoder to the decoder. The network also includes a multiscale discriminative network to discriminate better whether the image is a real high-quality or generated image. Additionally, an improved loss function is proposed to enhance the efficacy of image enhancement. Compared to state-of-the-art methods, the proposed approach achieves the highest performance in enhancing nighttime images, making them clearer and more natural.

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A presentation attack detection network based on dynamic convolution and multi-level feature fusion with security and reliability

Cheng

Zhou

Zhao

et al. 2023

Future Generation Computer Systems

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A Highway Traffic Image Enhancement Algorithm Based on Improved GAN in Complex Weather Conditions

Cited by 8 publications

References 38 publications

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Nighttime road scene image enhancement based on cycle-consistent generative adversarial network

A presentation attack detection network based on dynamic convolution and multi-level feature fusion with security and reliability

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