scholarly journals Intelligent Vehicle Design based on PaddlePaddle and Deep Learning

2021 ◽  
Vol 2132 (1) ◽  
pp. 012003
Author(s):  
Song He ◽  
Hao Xue ◽  
Lejiang Guo ◽  
Xin Chen ◽  
Jun Hu
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Learning Algorithm ◽  
Real Field ◽  
Intelligent Vehicle ◽  
Traffic Signs ◽  
Automatic Data ◽  
Scheme Design ◽  
Automatic Driving ◽  
Deep Learning Algorithm

Abstract ABSTRACT.In order to visualize the applications of deep learning based intelligent vehicle in the real field vividly, especially in the unmanned cases in which it realizes the integration of various technologies such as automatic data acquisition, data model construction, automatic curve detection, traffic signs recognition, verification of the unmanned driving, etc. A M-typed Model intelligent vehicle that is embedded with a high-performance board from Baidu named Edge Board is adopted by this study. The vehicle is trained under the PaddlePaddle deep learning frame and Baidu AI Studio Develop platform. Through the autonomous control scheme design and the non-stop study on the deep learning algorithm, an intelligent vehicle model based on PaddlePaddle deep learning is here. The vehicle has the function of automatic driving on the simulated track. In addition, it can distinguish several traffic signs and make feedbacks accordingly.

Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs

GEOMATICA ◽  
2021 ◽  
pp. 1-23
Author(s):  
Roholah Yazdan ◽  
Masood Varshosaz ◽  
Saied Pirasteh ◽  
Fabio Remondino
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Geometric Constraints ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Set ◽  
Traffic Signs ◽  
Deep Learning Algorithm ◽  
Comparison Results

Automatic detection and recognition of traffic signs from images is an important topic in many applications. At first, we segmented the images using a classification algorithm to delineate the areas where the signs are more likely to be found. In this regard, shadows, objects having similar colours, and extreme illumination changes can significantly affect the segmentation results. We propose a new shape-based algorithm to improve the accuracy of the segmentation. The algorithm works by incorporating the sign geometry to filter out the wrong pixels from the classification results. We performed several tests to compare the performance of our algorithm against those obtained by popular techniques such as Support Vector Machine (SVM), K-Means, and K-Nearest Neighbours. In these tests, to overcome the unwanted illumination effects, the images are transformed into colour spaces Hue, Saturation, and Intensity, YUV, normalized red green blue, and Gaussian. Among the traditional techniques used in this study, the best results were obtained with SVM applied to the images transformed into the Gaussian colour space. The comparison results also suggested that by adding the geometric constraints proposed in this study, the quality of sign image segmentation is improved by 10%–25%. We also comparted the SVM classifier enhanced by incorporating the geometry of signs with a U-Shaped deep learning algorithm. Results suggested the performance of both techniques is very close. Perhaps the deep learning results could be improved if a more comprehensive data set is provided.

scholarly journals Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of Plasmodium falciparum Genes

2021 ◽  
Vol 22 (18) ◽  
pp. 10019
Author(s):  
Apichat Suratanee ◽  
Kitiporn Plaimas
Keyword(s):  
Plasmodium Falciparum ◽  
Deep Learning ◽  
Unknown Function ◽  
Heterogeneous Network ◽  
High Performance ◽  
Striated Muscle ◽  
Learning Algorithm ◽  
Enrichment Analysis ◽  
Biomedical Science ◽  
Deep Learning Algorithm

Functional annotation of unknown function genes reveals unidentified functions that can enhance our understanding of complex genome communications. A common approach for inferring gene function involves the ortholog-based method. However, genetic data alone are often not enough to provide information for function annotation. Thus, integrating other sources of data can potentially increase the possibility of retrieving annotations. Network-based methods are efficient techniques for exploring interactions among genes and can be used for functional inference. In this study, we present an analysis framework for inferring the functions of Plasmodium falciparum genes based on connection profiles in a heterogeneous network between human and Plasmodium falciparum proteins. These profiles were fed into a hybrid deep learning algorithm to predict the orthologs of unknown function genes. The results show high performance of the model’s predictions, with an AUC of 0.89. One hundred and twenty-one predicted pairs with high prediction scores were selected for inferring the functions using statistical enrichment analysis. Using this method, PF3D7_1248700 and PF3D7_0401800 were found to be involved with muscle contraction and striated muscle tissue development, while PF3D7_1303800 and PF3D7_1201000 were found to be related to protein dephosphorylation. In conclusion, combining a heterogeneous network and a hybrid deep learning technique can allow us to identify unknown gene functions of malaria parasites. This approach is generalized and can be applied to other diseases that enhance the field of biomedical science.

User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Keondo Lee ◽  
Seong-Eun Kim ◽  
Junsang Doh ◽  
Keehoon Kim ◽  
Wan Kyun Chung
Keyword(s):  
Deep Learning ◽  
Piezoelectric Actuator ◽  
Cell Sorting ◽  
High Performance ◽  
Learning Algorithm ◽  
Cell Sorter ◽  
Operational Procedure ◽  
Deep Learning Algorithm ◽  
Sorting Technique ◽  
User Friendly

The image-activated cell sorter employs a significantly simplified operational procedure based on a syringe connected to a piezoelectric actuator and high-performance inference with TensorRT Integration.

scholarly journals Deep learning neural networks to differentiate Stafne’s bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254997
Author(s):  
Ari Lee ◽  
Min Su Kim ◽  
Sang-Sun Han ◽  
PooGyeon Park ◽  
Chena Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Imaging System ◽  
Panoramic Radiography ◽  
Learning Algorithm ◽  
Odontogenic Cysts ◽  
Surgical Removal ◽  
Imaging Features ◽  
Deep Learning Algorithm ◽  
Dentigerous Cysts

This study aimed to develop a high-performance deep learning algorithm to differentiate Stafne’s bone cavity (SBC) from cysts and tumors of the jaw based on images acquired from various panoramic radiographic systems. Data sets included 176 Stafne’s bone cavities and 282 odontogenic cysts and tumors of the mandible (98 dentigerous cysts, 91 odontogenic keratocysts, and 93 ameloblastomas) that required surgical removal. Panoramic radiographs were obtained using three different imaging systems. The trained model showed 99.25% accuracy, 98.08% sensitivity, and 100% specificity for SBC classification and resulted in one misclassified SBC case. The algorithm was approved to recognize the typical imaging features of SBC in panoramic radiography regardless of the imaging system when traced back with Grad-Cam and Guided Grad-Cam methods. The deep learning model for SBC differentiating from odontogenic cysts and tumors showed high performance with images obtained from multiple panoramic systems. The present algorithm is expected to be a useful tool for clinicians, as it diagnoses SBCs in panoramic radiography to prevent unnecessary examinations for patients. Additionally, it would provide support for clinicians to determine further examinations or referrals to surgeons for cases where even experts are unsure of diagnosis using panoramic radiography alone.

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