Rain sensor for automatic systems on vehicles

Vasile, Alexandru; Vasile, Irina; Nistor, Adrian; Vlădăreanu, Luige; Pantazică, Mihaela; Bonea, Andreea; Drumea, Andrei; Plotog, Ioan

doi:10.1117/12.882156

Cited by 5 publications

(2 citation statements)

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“…Notably, optoelectronic principles are employed in automotive rain sensors to regulate windshield wipers effectively. Using a discrete setup consisting of a transmitter, receiver, and a prism, one or multiple raindrops are detected on the glass surface based on intensity changes caused by light decoupling at the drop [3]. A similar concept is adopted for traditional optical level measurement.…”

Section: Introductionmentioning

confidence: 99%

Integrated optical fluid sensor in glass

Reitz,

Leineweber,

Overmeyer

2023

Optifab 2023

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Section: Introductionmentioning

confidence: 99%

Integrated optical fluid sensor in glass

Reitz,

Leineweber,

Overmeyer

2023

Optifab 2023

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“…Images with rain streaks are often captured by outdoor surveillance equipments, which may significantly degrade the performance of some existing computer vision systems and may also result in a pool visual experience for some multimedia applications. Automatic rain streaks removal has thus become a crucial research task in the field of computer vision and multimedia, and has been successfully applied in the fields of driverless technology [16,31] and content based image editing [10,28,34,39]. The research on visual de-raining can be traced back to the last decade.…”

Section: Introductionmentioning

confidence: 99%

Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining

Zhang

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

220

143

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Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow changes. In this paper, we propose a unified neural network module to address this problem, called Attentive Crowd Flow Machine (ACFM), which is able to infer the evolution of the crowd flow by learning dynamic representations of temporally-varying data with an attention mechanism. Specifically, the ACFM is composed of two progressive ConvL-STM units connected with a convolutional layer for spatial weight prediction. The first LSTM takes the sequential flow density representation as input and generates a hidden state at each time-step for attention map inference, while the second LSTM aims at learning the effective spatial-temporal feature expression from attentionally weighted crowd flow features. Based on the ACFM, we further build a deep architecture with the application to citywide crowd flow prediction, which naturally incorporates the sequential and periodic data as well as other external influences. Extensive experiments on two standard benchmarks (i.e., crowd flow in Beijing and New York City) show that the proposed method achieves significant improvements over the state-of-the-art methods.

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