Robust Pupil Segmentation and Center Detection from Visible Light Images Using Convolutional Neural Network

2018 ◽  
Author(s):  
Kazunari Kitazumi ◽  
Atsushi Nakazawa
Keyword(s):  
Neural Network ◽  
Visible Light ◽  
Convolutional Neural Network

Visible light polarization image desmogging via Cycle Convolutional Neural Network

2021 ◽  
Author(s):  
Jingjing Zhang ◽  
Xin Zhang ◽  
Teng Li ◽  
Yuzhou Zeng ◽  
Gang Lv ◽  
...  
Keyword(s):  
Neural Network ◽  
Visible Light ◽  
Convolutional Neural Network ◽  
Light Polarization ◽  
Polarization Image

scholarly journals Recognition of Damaged Arrow-Road Markings by Visible Light Camera Sensor Based on Convolutional Neural Network

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2160 ◽  
Author(s):  
Husan Vokhidov ◽  
Hyung Hong ◽  
Jin Kang ◽  
Toan Hoang ◽  
Kang Park
Keyword(s):  
Neural Network ◽  
Visible Light ◽  
Convolutional Neural Network ◽  
Camera Sensor

Winter Road Surface Conditions Classification using Convolutional Neural Network (CNN): Visible Light and Thermal Images Fusion

2021 ◽  
Author(s):  
Ce Zhang ◽  
Ehsan Nateghinia ◽  
Luis Miranda-Moreno ◽  
Lijun Sun
Keyword(s):  
Neural Network ◽  
Visible Light ◽  
Convolutional Neural Network ◽  
Weighted Average ◽  
Road Surface ◽  
Thermal Images ◽  
Winter Maintenance ◽  
Surface Conditions ◽  
Snow Plowing ◽  
Winter Road

In winter, road conditions play a crucial role in traffic flow efficiency and road safety. Icy, snowy, slushy, or wet road conditions reduce tire friction and affect vehicle stability which could lead to dangerous crashes. To keep traffic operations safe, cities spend a significant budget on winter maintenance operations such as snow plowing and spreading salt/sand. This paper proposes a methodology for automated winter road surface conditions classification using Convolutional Neural Network and the combination of thermal and visible light cameras. As part of this research, 4,244 pairs of visible light and thermal images are captured from pavement surfaces and classified into snowy, icy, wet, and slushy surface conditions. Two single-stream CNN models (visible light and thermal streams), and one dual-stream CNN model are developed. The average F1-Score of dual-stream model is 0.866, 0.935, 0.985, and 0.888 on snowy, icy, wet, and slushy, respectively. The weighted average F1-Score is 0.94.

scholarly journals Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2995 ◽  
Author(s):  
Se Cho ◽  
Na Baek ◽  
Min Kim ◽  
Ja Koo ◽  
Jong Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Visible Light ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Near Infrared ◽  
Histogram Equalization ◽  
Illumination Variation ◽  
Step Method ◽  
Long Distance ◽  
Surveillance Camera

Conventional nighttime face detection studies mostly use near-infrared (NIR) light cameras or thermal cameras, which are robust to environmental illumination variation and low illumination. However, for the NIR camera, it is difficult to adjust the intensity and angle of the additional NIR illuminator according to its distance from an object. As for the thermal camera, it is expensive to use as a surveillance camera. For these reasons, we propose a nighttime face detection method based on deep learning using a single visible-light camera. In a long-distance night image, it is difficult to detect faces directly from the entire image due to noise and image blur. Therefore, we propose Two-Step Faster region-based convolutional neural network (R-CNN) based on the image preprocessed by histogram equalization (HE). As a two-step scheme, our method sequentially performs the detectors of body and face areas, and locates the face inside a limited body area. By using our two-step method, the processing time by Faster R-CNN can be reduced while maintaining the accuracy of face detection by Faster R-CNN. Using a self-constructed database called Dongguk Nighttime Face Detection database (DNFD-DB1) and an open database of Fudan University, we proved that the proposed method performs better compared to other existing face detectors. In addition, the proposed Two-Step Faster R-CNN outperformed single Faster R-CNN and our method with HE showed higher accuracies than those without our preprocessing in nighttime face detection.

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