scholarly journals Airborne LiDAR

2021 ◽  
pp. 495-498
Author(s):  
Harsh Jindal ◽  
Jagdeep kaur
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Autonomous Vehicles ◽  
Airborne Lidar ◽  
Current State ◽  
Different Types ◽  
The Future ◽  
Data Capturing ◽  
Existing Data

This data has been used in many existing inventions and even in many new inventions. This paper consists of a review of the current state of LiDAR technology and covers issues related to both data capturing and processing. In this paper, a discussion on different types of LiDAR sensors including LiDAR for autonomous vehicles was also done. It also explains existing data techniques and also gives a view of CNN (Convolutional Neural Networks). This paper also discusses the autonomous LiDAR technique in detail. The paper also discusses the future scope of technology.

scholarly journals Road Characteristics Detection Based on Joint Convolutional Neural Networks with Adaptive Squares

2021 ◽  
Vol 10 (6) ◽  
pp. 377
Author(s):  
Chiao-Ling Kuo ◽  
Ming-Hua Tsai
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Autonomous Vehicles ◽  
Detection Accuracy ◽  
Geospatial Information ◽  
Combination Rules ◽  
And Performance ◽  
Road Characteristics ◽  
Machine Readable ◽  
Background Image

The importance of road characteristics has been highlighted, as road characteristics are fundamental structures established to support many transportation-relevant services. However, there is still huge room for improvement in terms of types and performance of road characteristics detection. With the advantage of geographically tiled maps with high update rates, remarkable accessibility, and increasing availability, this paper proposes a novel simple deep-learning-based approach, namely joint convolutional neural networks (CNNs) adopting adaptive squares with combination rules to detect road characteristics from roadmap tiles. The proposed joint CNNs are responsible for the foreground and background image classification and various types of road characteristics classification from previous foreground images, raising detection accuracy. The adaptive squares with combination rules help efficiently focus road characteristics, augmenting the ability to detect them and provide optimal detection results. Five types of road characteristics—crossroads, T-junctions, Y-junctions, corners, and curves—are exploited, and experimental results demonstrate successful outcomes with outstanding performance in reality. The information of exploited road characteristics with location and type is, thus, converted from human-readable to machine-readable, the results will benefit many applications like feature point reminders, road condition reports, or alert detection for users, drivers, and even autonomous vehicles. We believe this approach will also enable a new path for object detection and geospatial information extraction from valuable map tiles.

scholarly journals Predicting the future direction of cell movement with convolutional neural networks

2018 ◽  
Author(s):  
Shori Nishimoto ◽  
Yuta Tokuoka ◽  
Takahiro G Yamada ◽  
Noriko F Hiroi ◽  
Akira Funahashi
Keyword(s):  
Neural Networks ◽  
Cell Fate ◽  
Convolutional Neural Networks ◽  
Cell Shape ◽  
Cell Movement ◽  
Image Features ◽  
Morphological Features ◽  
Current Cell ◽  
The Future ◽  
Future Direction

SummaryImage-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell fate. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future cell fate from current cell shape, and can be used to automatically identify those morphological features that influence future cell fate.

Comparative Analysis Between Recurrent Convolutional and Convolutional Neural Networks for Horizon Detection

2020 ◽  
Vol 17 (9) ◽  
pp. 4364-4367
Author(s):  
Shreya Srinarasi ◽  
Seema Jahagirdar ◽  
Charan Renganathan ◽  
H. Mallika
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Autonomous Vehicles ◽  
Semantic Segmentation ◽  
Unmanned Vehicles ◽  
Obstacle Detection ◽  
Ground Vehicles ◽  
Safe Navigation ◽  
Horizon Line ◽  
Flight Parameters

The preliminary step in the navigation of Unmanned Vehicles is to detect and identify the horizon line. One method to locate the horizon and obstacles in an image is through a supervised learning, semantic segmentation algorithm using Neural Networks. Unmanned Aerial Vehicles (UAVs) are rapidly gaining prominence in military, commercial and civilian applications. For the safe navigation of UAVs, there poses a requirement for an accurate and efficient obstacle detection and avoidance. The position of the horizon and obstacles can also be used for adjusting flight parameters and estimating altitude. It can also be used for the navigation of Unmanned Ground Vehicles (UGV), by neglecting the part of the image above the horizon to reduce the processing time. Locating the horizon and identifying the various obstacles in an image can help in minimizing collisions and high costs due to failure of UAVs and UGVs. To achieve a robust and accurate system to aid navigation of autonomous vehicles, the efficiency and accuracy of Convolutional Neural Networks (CNN) and Recurrent-CNNs (RCNN) are analysed. It is observed via experimentation that the RCNN model classifies test images with higher accuracy.

scholarly journals Interpretable Compositional Convolutional Neural Networks

2021 ◽  
Author(s):  
Wen Shen ◽  
Zhihua Wei ◽  
Shikun Huang ◽  
Binbin Zhang ◽  
Jiaqi Fan ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Image Region ◽  
Visual Patterns ◽  
Different Types ◽  
Object Part

This paper proposes a method to modify a traditional convolutional neural network (CNN) into an interpretable compositional CNN, in order to learn filters that encode meaningful visual patterns in intermediate convolutional layers. In a compositional CNN, each filter is supposed to consistently represent a specific compositional object part or image region with a clear meaning. The compositional CNN learns from image labels for classification without any annotations of parts or regions for supervision. Our method can be broadly applied to different types of CNNs. Experiments have demonstrated the effectiveness of our method. The code will be released when the paper is accepted.

Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events

2021 ◽  
Vol 15 (02) ◽  
pp. 161-187
Author(s):  
Olav A. Nergård Rongved ◽  
Steven A. Hicks ◽  
Vajira Thambawita ◽  
Håkon K. Stensland ◽  
Evi Zouganeli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Event Detection ◽  
State Of The Art ◽  
Time Estimation ◽  
High Recall ◽  
Current State ◽  
Ablation Study ◽  
Different Parts

Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.

Deep imitation learning for autonomous vehicles based on convolutional neural networks

2020 ◽  
Vol 7 (1) ◽  
pp. 82-95 ◽  
Author(s):  
Parham M. Kebria ◽  
Abbas Khosravi ◽  
Syed Moshfeq Salaken ◽  
Saeid Nahavandi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Autonomous Vehicles ◽  
Imitation Learning

scholarly journals Predicting the future direction of cell movement with convolutional neural networks

PLoS ONE ◽  
2019 ◽  
Vol 14 (9) ◽  
pp. e0221245 ◽  
Author(s):  
Shori Nishimoto ◽  
Yuta Tokuoka ◽  
Takahiro G. Yamada ◽  
Noriko F. Hiroi ◽  
Akira Funahashi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cell Movement ◽  
The Future ◽  
Future Direction

scholarly journals Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection

2020 ◽  
Vol 6 (12) ◽  
pp. 129
Author(s):  
Mario Manzo ◽  
Simone Pellino
Keyword(s):  
Neural Networks ◽  
Skin Lesion ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Image Features ◽  
Ensemble Classification ◽  
Deep Convolutional Neural Networks ◽  
Melanoma Detection ◽  
Statistical Measures ◽  
Different Types

Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.

Sign in / Sign up

Export Citation Format

Share Document