scholarly journals LIDAR-BASED LANE MARKING EXTRACTION THROUGH INTENSITY THRESHOLDING AND DEEP LEARNING APPROACHES: A PAVEMENT-BASED ASSESSMENT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 507-514
Author(s):  
Y.-T. Cheng ◽  
A. Patel ◽  
D. Bullock ◽  
A. Habib
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Asphalt Pavement ◽  
Rapid Development ◽  
Point Clouds ◽  
Learning Model ◽  
Concrete Pavement ◽  
Concrete Pavements ◽  
Lane Marking ◽  
Deep Learning Model

Abstract. With the rapid development of autonomous vehicles (AV) and high-definition (HD) maps, up-to-date lane marking information is necessary. Over the years, several lane marking extraction approaches have been proposed with many of them based on accurate and dense Light Detection and Ranging (LiDAR) point cloud data collected by mobile mapping systems (MMS). This study proposes a normalized intensity thresholding strategy and a deep learning strategy with automatically generated labels. The former extracts lane markings directly from LiDAR point clouds while the latter utilizes 2D intensity images generated from the LiDAR point cloud. Additionally, the proposed approaches are also compared with state-of-the-art strategies such as original intensity thresholding and a deep learning approach based on manually established labels. Finally, each strategy is evaluated in asphalt and concrete pavements separately to assess their sensitivity to the nature of pavement surface. The results show that the deep learning model trained with automatically generated labels performs the best in both asphalt and concrete pavement area with an F1-score of 84.9% and 85.1%. In asphalt pavement area, original intensity thresholding strategy shows a lane marking extraction performance comparable to the other strategies while in concrete pavement area, it is significantly poor with an F1-score of 65.1%. Between the proposed normalized intensity thresholding and deep learning model trained with manually labeled data, the former performs better in asphalt pavement area while the latter obtains better results in concrete pavements.

Point Encoder GAN: A deep learning model for 3D point cloud inpainting

2020 ◽  
Vol 384 ◽  
pp. 192-199 ◽  
Author(s):  
Yikuan Yu ◽  
Zitian Huang ◽  
Fei Li ◽  
Haodong Zhang ◽  
Xinyi Le
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Learning Model ◽  
3D Point Cloud ◽  
Deep Learning Model

scholarly journals Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds

2020 ◽  
Vol 12 (9) ◽  
pp. 1379 ◽  
Author(s):  
Yi-Ting Cheng ◽  
Ankit Patel ◽  
Chenglu Wen ◽  
Darcy Bullock ◽  
Ayman Habib
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Point Clouds ◽  
Concrete Pavement ◽  
Training Data ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
Lane Marking ◽  
Wide Range ◽  
Lane Width

Lane markings are one of the essential elements of road information, which is useful for a wide range of transportation applications. Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging (LiDAR) point clouds acquired by mobile mapping systems (MMS). This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction. For comparative evaluation, original intensity thresholding and deep learning using manually established labels strategies are also implemented. A pavement surface-based assessment of lane marking extraction by the four strategies is conducted in asphalt and concrete pavement areas covered by MMS equipped with multiple LiDAR scanners. Additionally, the extracted lane markings are used for lane width estimation and reporting lane marking gaps along various highways. The normalized intensity thresholding leads to a better lane marking extraction with an F1-score of 78.9% in comparison to the original intensity thresholding with an F1-score of 72.3%. On the other hand, the deep learning model trained with automatically generated labels achieves a higher F1-score of 85.9% than the one trained on manually established labels with an F1-score of 75.1%. In concrete pavement area, the normalized intensity thresholding and both deep learning strategies obtain better lane marking extraction (i.e., lane markings along longer segments of the highway have been extracted) than the original intensity thresholding approach. For the lane width results, more estimates are observed, especially in areas with poor edge lane marking, using the two deep learning models when compared with the intensity thresholding strategies due to the higher recall rates for the former. The outcome of the proposed strategies is used to develop a framework for reporting lane marking gap regions, which can be subsequently visualized in RGB imagery to identify their cause.

Medical Image Classification based on an Adaptive Size Deep Learning Model

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-18
Author(s):  
Xiangbin Liu ◽  
Jiesheng He ◽  
Liping Song ◽  
Shuai Liu ◽  
Gautam Srivastava
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Medical Image ◽  
Rapid Development ◽  
Learning Model ◽  
Fine Tuning ◽  
Optimal Size ◽  
Image Dataset ◽  
Medical Image Classification ◽  
Deep Learning Model

With the rapid development of Artificial Intelligence (AI), deep learning has increasingly become a research hotspot in various fields, such as medical image classification. Traditional deep learning models use Bilinear Interpolation when processing classification tasks of multi-size medical image dataset, which will cause the loss of information of the image, and then affect the classification effect. In response to this problem, this work proposes a solution for an adaptive size deep learning model. First, according to the characteristics of the multi-size medical image dataset, the optimal size set module is proposed in combination with the unpooling process. Next, an adaptive deep learning model module is proposed based on the existing deep learning model. Then, the model is fused with the size fine-tuning module used to process multi-size medical images to obtain a solution of the adaptive size deep learning model. Finally, the proposed solution model is applied to the pneumonia CT medical image dataset. Through experiments, it can be seen that the model has strong robustness, and the classification effect is improved by about 4% compared with traditional algorithms.

scholarly journals Research on Students’ Adaptive Learning System Based on Deep Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xi Yang ◽  
Zhihan Zhou ◽  
Yu Xiao
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Adaptive Learning ◽  
Rapid Development ◽  
Learning Model ◽  
Learning System ◽  
Learning Resources ◽  
Recommendation Algorithm ◽  
Adaptive Learning System ◽  
Deep Learning Model

With the rapid development of deep learning in recent years, recommendation algorithm combined with deep learning model has become an important direction in the field of recommendation in the future. Personalized learning resource recommendation is the main way to realize students’ adaptation to the learning system. Based on the in-depth learning mode, students’ online learning action data are obtained, and further learning analysis technology is used to construct students’ special learning mode and provide suitable learning resources. The traditional method of introducing learning resources mainly stays at the level of examination questions. What ignores the essence of students’ learning is the learning of knowledge points. Students’ learning process is affected by “before” and “after” learning behavior, which has the characteristics of time. Among them, bidirectional length cyclic neural network is good at considering the “front” and “back” states of recommended nodes when recommending prediction results. For the above situation, this paper proposes a recommendation method of students’ learning resources based on bidirectional long-term and short-term memory cyclic neural network. Firstly, recommend the second examination according to the knowledge points, predict the scores of important steps including the accuracy of the recommended examination of the target students and the knowledge points of the recommended examination, and finally cooperate with the above two prediction results to judge whether the examination questions are finally recommended. Through the comparative experiment with the traditional recommendation algorithm, it is found that the student adaptive learning system based on the deep learning model proposed in this paper has better stability and interpretability in the recommendation results.

Deep Learning Model for Point Cloud Classification Based on Graph Convolutional Network

2019 ◽  
Vol 56 (21) ◽  
pp. 211004
Author(s):  
王旭娇 Wang Xujiao ◽  
马杰 Ma Jie ◽  
王楠楠 Wang Nannan ◽  
马鹏飞 Ma Pengfei ◽  
杨立闯 Yang Lichaung
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Learning Model ◽  
Convolutional Network ◽  
Cloud Classification ◽  
Point Cloud Classification ◽  
Deep Learning Model

scholarly journals Patent Analysis Using Vector Space Model and Deep Learning Model : A Case of Artificial Intelligence Industry Technology

2021 ◽  
Author(s):  
Yongmin Yoo ◽  
Dongjin Lim ◽  
Kyungsun Kim
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Vector Space ◽  
Rapid Development ◽  
Vector Space Model ◽  
Learning Model ◽  
Patent Data ◽  
Space Model ◽  
Artificial Intelligence Technology ◽  
Deep Learning Model

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

Sign in / Sign up

Export Citation Format

Share Document