Keratoconus Detection Algorithm using Convolutional Neural Networks: Challenges

2019 ◽  
Author(s):  
Alexandru Lavric ◽  
Valentin Popa ◽  
Cristina David ◽  
Cristian Costel Paval
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection Algorithm

scholarly journals Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

2021 ◽  
Vol 11 (13) ◽  
pp. 5931
Author(s):  
Ji’an You ◽  
Zhaozheng Hu ◽  
Chao Peng ◽  
Zhiqiang Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Annotation ◽  
Three Dimensional ◽  
Image Data ◽  
Detection Algorithm ◽  
Simulation Software ◽  
High Quality ◽  
Annotation Method ◽  
Detection Of Objects

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.

scholarly journals Illumination-robust face recognition based on deep convolutional neural networks architectures

2020 ◽  
Vol 18 (2) ◽  
pp. 1015
Author(s):  
Ridha Ilyas Bendjillali ◽  
Mohammed Beladgham ◽  
Khaled Merit ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Feature Learning ◽  
Histogram Equalization ◽  
Detection Algorithm ◽  
Deep Convolutional Neural Networks ◽  
Biometric Technology ◽  
Equalization Algorithm ◽  
Robust Face Recognition

<p><span>In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classification. For learning the features followed by classification we used VGG16, ResNet50 and Inception-v3 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Inception-v3 architecture has achieved a rate of 99, 44% and 99, 89% respectively.</span></p>

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

2021 ◽  
Author(s):  
Kianoosh Kazemi ◽  
Juho Laitala ◽  
Iman Azimi ◽  
Pasi Liljeberg ◽  
Amir M. Rahmani
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Detection Methods ◽  
Method Performance ◽  
Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

scholarly journals Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction

2019 ◽  
Vol 9 (14) ◽  
pp. 2865 ◽  
Author(s):  
Kyungmin Jo ◽  
Yuna Choi ◽  
Jaesoon Choi ◽  
Jong Woo Chung
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Motion Vector ◽  
Detection Algorithm ◽  
Surgical Instruments ◽  
The Mean ◽  
Real Time Detection ◽  
Motion Vector Prediction

More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).

scholarly journals Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3

2019 ◽  
Author(s):  
Adel Ammar ◽  
Anis Koubaa ◽  
Mohanned Ahmed ◽  
Abdulrahman Saad
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Detection Algorithm ◽  
Aerial Images ◽  
Object Size ◽  
Car Detection ◽  
Images Processing ◽  
Different Characteristics ◽  
The Impact

In this paper, we address the problem of car detection from aerial images using Convolutional Neural Networks (CNN). This problem presents additional challenges as compared to car (or any object) detection from ground images because features of vehicles from aerial images are more difficult to discern. To investigate this issue, we assess the performance of two state-of-the-art CNN algorithms, namely Faster R-CNN, which is the most popular region-based algorithm, and YOLOv3, which is known to be the fastest detection algorithm. We analyze two datasets with different characteristics to check the impact of various factors, such as UAV&rsquo;s altitude, camera resolution, and object size. The objective of this work is to conduct a robust comparison between these two cutting-edge algorithms. By using a variety of metrics, we show that none of the two algorithms outperforms the other in all cases.

scholarly journals Keypoint-Based Robotic Grasp Detection Scheme in Multi-Object Scenes

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2132
Author(s):  
Tong Li ◽  
Fei Wang ◽  
Changlei Ru ◽  
Yong Jiang ◽  
Jinghong Li
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Single Point ◽  
Detection Algorithm ◽  
Success Rates ◽  
Single Object ◽  
Detection Scheme ◽  
Intelligent Robots ◽  
Center Point ◽  
Robot Grasping

Robot grasping is an important direction in intelligent robots. However, how to help robots grasp specific objects in multi-object scenes is still a challenging problem. In recent years, due to the powerful feature extraction capabilities of convolutional neural networks (CNN), various algorithms based on convolutional neural networks have been proposed to solve the problem of grasp detection. Different from anchor-based grasp detection algorithms, in this paper, we propose a keypoint-based scheme to solve this problem. We model an object or a grasp as a single point—the center point of its bounding box. The detector uses keypoint estimation to find the center point and regress to all other object attributes such as size, direction, etc. Experimental results demonstrate that the accuracy of this method is 74.3% in the multi-object grasp dataset VMRD, and the performance on the single-object scene Cornell dataset is competitive with the current state-of-the-art grasp detection algorithm. Robot experiments demonstrate that this method can help robots grasp the target in single-object and multi-object scenes with overall success rates of 94% and 87%, respectively.

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

2021 ◽  
Author(s):  
Kianoosh Kazemi ◽  
Juho Laitala ◽  
Iman Azimi ◽  
Pasi Liljeberg ◽  
Amir M. Rahmani
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Detection Methods ◽  
Method Performance ◽  
Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

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