scholarly journals Real-Time Automatic Investigation of Indian Roadway Animals by 3D Reconstruction Detection Using Deep Learning for R-3D-YOLOV3 Image Classification and Filtering

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3079
Author(s):  
Sudhakar Sengan ◽  
Ketan Kotecha ◽  
Indragandhi Vairavasundaram ◽  
Priya Velayutham ◽  
Vijayakumar Varadarajan ◽  
...  
Keyword(s):  
Deep Learning ◽  
3D Reconstruction ◽  
Real Time ◽  
Closed Loop ◽  
Multiple Views ◽  
Wild Animals ◽  
Road Accidents ◽  
Object Properties ◽  
Real Time Applications ◽  
Iot Devices

Statistical reports say that, from 2011 to 2021, more than 11,915 stray animals, such as cats, dogs, goats, cows, etc., and wild animals were wounded in road accidents. Most of the accidents occurred due to negligence and doziness of drivers. These issues can be handled brilliantly using stray and wild animals-vehicle interaction and the pedestrians’ awareness. This paper briefs a detailed forum on GPU-based embedded systems and ODT real-time applications. ML trains machines to recognize images more accurately than humans. This provides a unique and real-time solution using deep-learning real 3D motion-based YOLOv3 (DL-R-3D-YOLOv3) ODT of images on mobility. Besides, it discovers methods for multiple views of flexible objects using 3D reconstruction, especially for stray and wild animals. Computer vision-based IoT devices are also besieged by this DL-R-3D-YOLOv3 model. It seeks solutions by forecasting image filters to find object properties and semantics for object recognition methods leading to closed-loop ODT.

scholarly journals Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 982 ◽  
Author(s):  
Hyo Lee ◽  
Ihsan Ullah ◽  
Weiguo Wan ◽  
Yongbin Gao ◽  
Zhijun Fang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Recognition Rate ◽  
Experimental Results ◽  
Learning Approach ◽  
Deep Model ◽  
Proposed Model ◽  
Real Time Applications ◽  
Model Recognition

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

scholarly journals An Intelligent Microwave Oven with Thermal Imaging and Temperature Recommendation Using Deep Learning

2020 ◽  
Vol 3 (1) ◽  
pp. 13 ◽  
Author(s):  
Tareq Khan
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Thermal Imaging ◽  
Closed Loop ◽  
Thermal Image ◽  
Microwave Oven ◽  
Closed Loop Control ◽  
Target Temperature ◽  
Thermal Camera ◽  
Loop Control

Whenever food in a microwave oven is heated, the user estimates the time to heat. This estimation can be incorrect, leading the food to be too hot or still cold. In this research, an intelligent microwave oven is designed. After the food is put into the microwave oven and the door is closed, it captures the image of the food, classifies the image and then suggests the food’s target temperature by learning from previous experiences, so the user does not have to recall the target food temperature each time the same food is warmed. The temperature of the food is measured using a thermal camera. The proposed microwave incorporates a display to show a real-time colored thermal image of the food. The microwave automatically stops the heating when the temperature of the food hits the target temperature using closed-loop control. The deep learning-based image classifier gradually learns the type of foods that are consumed in that household and becomes smarter in temperature recommendation. The system can classify and recommend target temperature with 93% accuracy. A prototype is developed using a microcontroller-based system and successfully tested.

Real-Time Road Monitoring Using Deep Learning Algorithm Deployed on IoT Devices

2021 ◽  
pp. 137-147
Author(s):  
Nilay Nishant ◽  
Ashish Maharjan ◽  
Dibyajyoti Chutia ◽  
P. L. N. Raju ◽  
Ashis Pradhan
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Iot Devices

Boosting the deep learning wavefront sensor for real-time applications [Invited]

2021 ◽  
Vol 60 (10) ◽  
pp. B119
Author(s):  
Esteban Vera ◽  
Felipe Guzmán ◽  
Camilo Weinberger
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Wavefront Sensor ◽  
Real Time Applications

scholarly journals DANAE++: A Smart Approach for Denoising Underwater Attitude Estimation

2021 ◽  
Author(s):  
Paolo Russo ◽  
Fabiana Di Ciaccio ◽  
Salvatore Troisi
Keyword(s):  
Deep Learning ◽  
Kalman Filter ◽  
Real Time ◽  
Denoising Autoencoder ◽  
Orientation Estimation ◽  
Underwater Robots ◽  
Underwater Environment ◽  
Real Time Applications ◽  
And Performance ◽  
Vehicle Positioning

One of the main issues for underwater robots navigation is represented by the accurate vehicle positioning, which heavily depends on the orientation estimation phase. The systems employed to this scope are affected by different noise typologies, mainly related to the sensors and to the irregular noise of the underwater environment. Filtering algorithms can reduce their effect if opportunely configured, but this process usually requires fine techniques and time. This paper presents DANAE++, an improved denoising autoencoder based on DANAE, which is able to recover Kalman Filter IMU/AHRS orientation estimations from any kind of noise, independently of its nature. This deep learning-based architecture already proved to be robust and reliable, but in its enhanced implementation significant improvements are obtained both in terms of results and performance. In fact, DANAE++is able to denoise the three angles describing the attitude at the same time, and that is verified also on the estimations provided by the more performing Extended KF. Further tests could make this method suitable for real-time applications on navigation tasks.

Virtual Reality Aided High-Quality 3D Reconstruction by Remote Drones

2022 ◽  
Vol 22 (1) ◽  
pp. 1-20
Author(s):  
Di Zhang ◽  
Feng Xu ◽  
Chi-Man Pun ◽  
Yang Yang ◽  
Rushi Lan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
3D Reconstruction ◽  
Real Time ◽  
Target Object ◽  
Measurement Unit ◽  
Post Processing ◽  
Camera Tracking ◽  
Wireless Data Transmission ◽  
Reconstruction Methods

Artificial intelligence including deep learning and 3D reconstruction methods is changing the daily life of people. Now, an unmanned aerial vehicle that can move freely in the air and avoid harsh ground conditions has been commonly adopted as a suitable tool for 3D reconstruction. The traditional 3D reconstruction mission based on drones usually consists of two steps: image collection and offline post-processing. But there are two problems: one is the uncertainty of whether all parts of the target object are covered, and another is the tedious post-processing time. Inspired by modern deep learning methods, we build a telexistence drone system with an onboard deep learning computation module and a wireless data transmission module that perform incremental real-time dense reconstruction of urban cities by itself. Two technical contributions are proposed to solve the preceding issues. First, based on the popular depth fusion surface reconstruction framework, we combine it with a visual-inertial odometry estimator that integrates the inertial measurement unit and allows for robust camera tracking as well as high-accuracy online 3D scan. Second, the capability of real-time 3D reconstruction enables a new rendering technique that can visualize the reconstructed geometry of the target as navigation guidance in the HMD. Therefore, it turns the traditional path-planning-based modeling process into an interactive one, leading to a higher level of scan completeness. The experiments in the simulation system and our real prototype demonstrate an improved quality of the 3D model using our artificial intelligence leveraged drone system.

scholarly journals Internet of Things (IoT) Authentication and Access Control by Hybrid Deep Learning Method - A Study

2021 ◽  
Vol 2 (4) ◽  
pp. 236-245
Author(s):  
Joy Iong Zong Chen ◽  
Kong-Long Lai
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Control Device ◽  
Security And Privacy ◽  
The Real ◽  
Intelligent Technique ◽  
History Of ◽  
Iot Devices ◽  
Future Direction

In the history of device computing, Internet of Things (IoT) is one of the fastest growing field that facing many security challenges. The effective efforts should have been made to address the security and privacy issues in IoT networks. The IoT devices are basically resource control device which provide routine attract impression for cyber attackers. The IoT participation nodes are increasing rapidly with more resource constrained that creating more challenging conditions in the real time. The existing methods provide an ineffective response to the tasks for effective IoT device. Also, it is an insufficient to involve the complete security and safety spectrum of the IoT networks. Because of the existing algorithms are not enriched to secure IoT bionetwork in the real time environment. The existing system is not enough to detect the proxy to the authorized person in the embedding devices. Also, those methods are believed in single model domain. Therefore, the effectiveness is dropping for further multimodal domain such as combination of behavioral and physiological features. The embedding intelligent technique will be securitizing for the IoT devices and networks by deep learning (DL) techniques. The DL method is addressing different security and safety problems arise in real time environment. This paper is highlighting hybrid DL techniques with Reinforcement Learning (RL) for the better performance during attack and compared with existing one. Also, here we discussed about DL combined with RL of several techniques and identify the higher accuracy algorithm for security solutions. Finally, we discuss the future direction of decision making of DL based IoT security system.

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