Video-Based Windshield Rain Detection and Wiper Control Using Holistic-View Deep Learning

Lai, Chi-Cheng; Li, Chih-Hung G.

doi:10.1109/coase.2019.8843331

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…In [63], a deep convolutional neural network (CNN) is used to measure visibility whether a wiper is needed, and a pretrained "ResNet" model is used for rain detection. Te day experiments yielded a precision result of 64.4%, while the night experiment yielded a precision of 53.5%.…”

Section: Deepmentioning

confidence: 99%

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Driving in the Rain: A Survey toward Visibility Estimation through Windshields

Morden,

Caraffini,

Kypraios

et al. 2023

International Journal of Intelligent Systems

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Rain can significantly impair the driver’s sight and affect his performance when driving in wet conditions. Evaluation of driver visibility in harsh weather, such as rain, has garnered considerable research since the advent of autonomous vehicles and the emergence of intelligent transportation systems. In recent years, advances in computer vision and machine learning led to a significant number of new approaches to address this challenge. However, the literature is fragmented and should be reorganised and analysed to progress in this field. There is still no comprehensive survey article that summarises driver visibility methodologies, including classic and recent data-driven/model-driven approaches on the windshield in rainy conditions, and compares their generalisation performance fairly. Most ADAS and AD systems are based on object detection. Thus, rain visibility plays a key role in the efficiency of ADAS/AD functions used in semi- or fully autonomous driving. This study fills this gap by reviewing current state-of-the-art solutions in rain visibility estimation used to reconstruct the driver’s view for object detection-based autonomous driving. These solutions are classified as rain visibility estimation systems that work on (1) the perception components of the ADAS/AD function, (2) the control and other hardware components of the ADAS/AD function, and (3) the visualisation and other software components of the ADAS/AD function. Limitations and unsolved challenges are also highlighted for further research.

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Section: Deepmentioning

confidence: 99%

“…In [63], images from the perspective of a driver of the entire windshield are trained both for day and night. Te intensity of the rain is mentioned; however, the data are collected manually from online sources and are considered to activate the wiper.…”

Section: Perception and Visualisationmentioning

confidence: 99%

Driving in the Rain: A Survey toward Visibility Estimation through Windshields

Morden,

Caraffini,

Kypraios

et al. 2023

International Journal of Intelligent Systems

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“…Recently, with the advancement of Graphic Processing Unit (GPU) technology, deep learning algorithms detection technologies have attracted attention that can precisely analyze various objects and situations in complex environments. Chi Cheng Lai et al [19] developed a windshield rain detection system using deep learning on more than 150 k global background images of rainy situations. Huanjie Tao et al [20] developed a pixellevel supervised learning neural network to build an advanced detection system that can recognize forest smoke.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Multiple Droplet Contamination Detector for Vision Systems Using a You Only Look Once Algorithm

Kim,

Yoon

et al. 2024

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This paper presents a practical contamination detection system for camera lenses using image analysis with deep learning. The proposed system can detect contamination in camera digital images through contamination learning utilizing deep learning, and it aims to prevent performance degradation of intelligent vision systems due to lens contamination in cameras. This system is based on the object detection algorithm YOLO (v5n, v5s, v5m, v5l, and v5x), which is trained with 4000 images captured under different lighting and background conditions. The trained models showed that the average precision improves as the algorithm size increases, especially for YOLOv5x, which showed excellent efficiency in detecting droplet contamination within 23 ms. They also achieved an average precision (mAP@0.5) of 87.46%, recall (mAP@0.5:0.95) of 51.90%, precision of 90.28%, recall of 81.47%, and F1 score of 85.64%. As a proof of concept, we demonstrated the identification and removal of contamination on camera lenses by integrating a contamination detection system and a transparent heater-based cleaning system. The proposed system is anticipated to be applied to autonomous driving systems, public safety surveillance cameras, environmental monitoring drones, etc., to increase operational safety and reliability.

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