A study on thermal image generation based on deep learning and abnormal temperature detection

2020 ◽  
Author(s):  
Ziyun Zhang ◽  
Makoto Hasegawa
Keyword(s):  
Deep Learning ◽  
Thermal Image ◽  
Image Generation ◽  
Temperature Detection ◽  
Abnormal Temperature

Exploring Techniques for Photo-realistic Image Generation from 3D Models - A Deep Learning Approach

2021 ◽  
Author(s):  
Pranoy Ghosh ◽  
Krithika M Pai ◽  
Manohara Pai M M ◽  
Ujjwal Verma ◽  
Frederic Rivet ◽  
...  
Keyword(s):  
Deep Learning ◽  
3D Models ◽  
Learning Approach ◽  
Image Generation ◽  
Realistic Image

scholarly journals Light People: Professor Aydogan Ozcan

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Tingting Sun
Keyword(s):  
Deep Learning ◽  
Los Angeles ◽  
Learning Algorithm ◽  
Computational Imaging ◽  
Technological Innovations ◽  
Image Generation ◽  
Sensing Technology ◽  
Reconstruction Methods ◽  
New Ideas ◽  
The University

EditorialIn 2016, the news that Google’s artificial intelligence (AI) robot AlphaGo, based on the principle of deep learning, won the victory over lee Sedol, the former world Go champion and the famous 9th Dan competitor of Korea, caused a sensation in both fields of AI and Go, which brought epoch-making significance to the development of deep learning. Deep learning is a complex machine learning algorithm that uses multiple layers of artificial neural networks to automatically analyze signals or data. At present, deep learning has penetrated into our daily life, such as the applications of face recognition and speech recognition. Scientists have also made many remarkable achievements based on deep learning. Professor Aydogan Ozcan from the University of California, Los Angeles (UCLA) led his team to research deep learning algorithms, which provided new ideas for the exploring of optical computational imaging and sensing technology, and introduced image generation and reconstruction methods which brought major technological innovations to the development of related fields. Optical designs and devices are moving from being physically driven to being data-driven. We are much honored to have Aydogan Ozcan, Fellow of the National Academy of Inventors and Chancellor’s Professor of UCLA, to unscramble his latest scientific research results and foresight for the future development of related fields, and to share his journey of pursuing Optics, his indissoluble relationship with Light: Science & Applications (LSA), and his experience in talent cultivation.

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.

scholarly journals Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

2021 ◽  
Vol 7 (9) ◽  
pp. 176
Author(s):  
Daniel Queirós da Silva ◽  
Filipe Neves dos Santos ◽  
Armando Jorge Sousa ◽  
Vítor Filipe
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Forest Inventory ◽  
Mobile Robotics ◽  
Ground Level ◽  
Forest Tree ◽  
Thermal Image ◽  
Detection Methods ◽  
Site Management ◽  
Forest Wildfires

Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.

scholarly journals Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions

2021 ◽  
Vol 14 (1) ◽  
pp. 106
Author(s):  
Cheng Chen ◽  
Sindhu Chandra ◽  
Yufan Han ◽  
Hyungjoon Seo
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Defect Detection ◽  
Input Data ◽  
Thermal Image ◽  
Detection Accuracy ◽  
Detection Model ◽  
Fused Image ◽  
Data Source ◽  
Pavement Damage

Automatic damage detection using deep learning warrants an extensive data source that captures complex pavement conditions. This paper proposes a thermal-RGB fusion image-based pavement damage detection model, wherein the fused RGB-thermal image is formed through multi-source sensor information to achieve fast and accurate defect detection including complex pavement conditions. The proposed method uses pre-trained EfficientNet B4 as the backbone architecture and generates an argument dataset (containing non-uniform illumination, camera noise, and scales of thermal images too) to achieve high pavement damage detection accuracy. This paper tests separately the performance of different input data (RGB, thermal, MSX, and fused image) to test the influence of input data and network on the detection results. The results proved that the fused image’s damage detection accuracy can be as high as 98.34% and by using the dataset after augmentation, the detection model deems to be more stable to achieve 98.35% precision, 98.34% recall, and 98.34% F1-score.

scholarly journals Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

2021 ◽  
Vol 13 (1) ◽  
pp. 28-39
Author(s):  
Arshia Foroozan Yazdani ◽  
◽  
Ali Bozorgi Mehr ◽  
Iman Showkatyan ◽  
Amin Hashemi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Fluid Temperature ◽  
Temperature Detection

scholarly journals Temperature Measurement and Control Application in a Laser Plastic Surgery Real Temperature Detection System

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Hong Wei ◽  
Fang Jiang ◽  
Fang Shao ◽  
Denghui Zhang ◽  
Fang Gu ◽  
...  
Keyword(s):  
Temperature Measurement ◽  
Plastic Surgery ◽  
Detection System ◽  
Thermal Image ◽  
Measurement Technology ◽  
Thermal Imagery ◽  
Temperature Detection ◽  
Significant Difference ◽  
And Control ◽  
Measurement And Control

The purpose of this study was to grasp the development process of thermal image temperature measurement technology. It provides directional support for the optimization development of the thermal imagery and laser plastic surgery and laser treatment. This paper uses the infrared thermal image temperature measurement principle and performs infrared thermal image precise temperature measurement technology and its application research. The results showed that there was a correlation between 595 nm pulse dye laser, age, laser energy density, and skin temperature ( P < 0.05 ). There is a significant difference in the average ( P < 0.05 ). The infrared thermal imagery temperature monitoring system is a simple and relatively accurate temperature detection system that can be widely used in temperature measurement and control of laser plastic surgery.

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