Computer Vision System for Railway Track Crack Detection using Deep Learning Neural Network

2021 ◽  
Author(s):  
R. Thendral ◽  
A. Ranjeeth
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Crack Detection ◽  
Vision System ◽  
Railway Track ◽  
Computer Vision System ◽  
Deep Learning Neural Network

scholarly journals Automatic Asbestos Control Using Deep Learning Based Computer Vision System

2021 ◽  
Vol 11 (22) ◽  
pp. 10532
Author(s):  
Vasily Zyuzin ◽  
Mikhail Ronkin ◽  
Sergey Porshnev ◽  
Alexey Kalmykov
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Network Architecture ◽  
Near Infrared ◽  
Vision System ◽  
Open Pit ◽  
Infrared Range ◽  
Computer Vision System ◽  
Productivity Estimation

The paper discusses the results of the research and development of an innovative deep learning-based computer vision system for the fully automatic asbestos content (productivity) estimation in rock chunk (stone) veins in an open pit and within the time comparable with the work of specialists (about 10 min per one open pit processing place). The discussed system is based on the applying of instance and semantic segmentation of artificial neural networks. The Mask R-CNN-based network architecture is applied to the asbestos-containing rock chunks searching images of an open pit. The U-Net-based network architecture is applied to the segmentation of asbestos veins in the images of selected rock chunks. The designed system allows an automatic search and takes images of the asbestos rocks in an open pit in the near-infrared range (NIR) and processes the obtained images. The result of the system work is the average asbestos content (productivity) estimation for each controlled open pit. It is validated to estimate asbestos content as the graduated average ratio of the vein area value to the selected rock chunk area value, both determined by the trained neural network. For both neural network training tasks the training, validation, and test datasets are collected. The designed system demonstrates an error of about 0.4% under different weather conditions in an open pit when the asbestos content is about 1.5–4%. The obtained accuracy is sufficient to use the system as a geological service tool instead of currently applied visual-based estimations.

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

scholarly journals Computer vision based obstacle detection and target tracking for autonomous vehicles

2021 ◽  
Vol 336 ◽  
pp. 07004
Author(s):  
Ruoyu Fang ◽  
Cheng Cai
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Target Tracking ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Pid Algorithm ◽  
Deep Learning Neural Network

Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.

scholarly journals A computer vision system for deep learning-based detection of patient mobilization activities in the ICU

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Serena Yeung ◽  
Francesca Rinaldo ◽  
Jeffrey Jopling ◽  
Bingbin Liu ◽  
Rishab Mehra ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Computer Vision System

scholarly journals Segmentation for Complex Background Images using Deep Learning Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 1746-1750
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Vision System ◽  
Automated Segmentation ◽  
Computer Vision System ◽  
Important Stage ◽  
Deep Learning Algorithm ◽  
Complex Background ◽  
Learning Techniques

Segmentation is an important stage in any computer vision system. Segmentation involves discarding the objects which are not of our interest and extracting only the object of our interest. Automated segmentation has become very difficult when we have complex background and other challenges like illumination, occlusion etc. In this project we are designing an automated segmentation system using deep learning algorithm to segment images with complex background.

scholarly journals SMART CANTEEN : PERILAKU MENGATUR POLA MAKAN DENGAN MEMBACA NILAI NUTRISI (CONVENTIONAL DEEP LEARNING NEURAL NETWORK)

2020 ◽  
Vol 6 (2) ◽  
pp. 115-121
Author(s):  
Ari Purno Wahyu ◽  
Heri Heryono ◽  
Muhammad Benny Chaniago ◽  
Dani Hamdani
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Deep Learning Neural Network

Kesehatan merupakan bagian terpenting bagi kita dimana pengaruh atau datangnya penyakit melalui pola makan, terlebih bagi kita yang memiliki kesibukan yang luar biasa padatnya tentu saja tidak ada waktu untuk sarapan dan lebih memilih makanan cepat saji yang tersedia banyak di kantin atau kafe. Hal ini bukan berarti makanan cepat saji tidak sehat, hal ini akan menjadi masalah jika terlalu berlebih dan tidak memperhatikan takaran saji atau kandungan nutrisi yang ada pada makanan tersebut. Beberapa cara bisa dilakukan dengan menjaga sikap  pola makan misalkan dengan diet atau menggunakan aplikasi perhitungan nutrisi yang ada di pasaran dan gratis untuk diunduh. Jenis aplikasi ini masih kurang efektif dimana aplikasi tersebut masih merupakan perkiraan saja dan tidak bisa digunakan secara realtime. Penelitian sebelumnya bisa menggunakan teknik computer vision dengan menggunakan image sebagai alat pembaca dari makanan yang akan kita santap. Aplikasi tersebut mampu membaca kandungan nutrisi sekaligus  harga makanan, teknik pengolah image yang digunakan menggunakan metode Deep Learning Neural Network, algoritma ini terbukti memiliki akurasi dan pembacaan data yang tinggi dibandingkan algoritma yang lain. Aplikasi dengan Neural Network yang berbasis image bisa diimplementasikan pada mesin kasir di kantin atau cafe dan bisa dibuat dalam bentuk perangkat mobile sehingga lebih mudah digunakan. Teknik komputerisasi dengan Deep Learning Neural Network terbukti bisa diterapkan di kantin dan caf

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