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[Virtual Presenter] Good morning everyone, Today I will be talking about a research paper conducted by Ruturaj Samantaray, Asim Swarup, Soham Rath, Ankur Kumari and Jyoti Swain from C V Raman Global University in Bhubaneswar, India. The research paper I am going to present today is titled "Image Processing in Underground Harsh Environments". The paper discusses the development of a proof of concept (POC) to test and demonstrate the performance and reliability of an imaging system for use in harsh underground environmental conditions such as low light, dust, smoke, or fog, utilizing IoT sensors, YOLO and HOG techniques, and other components. Let's begin..

[Audio] This research paper investigates how image processing can be utilized in underground harsh conditions. Ruturaj Samantaray, Asim Swarup, Soham Rath, Ankur Kumari and Jyoti Swain from C V Raman Global University in Bhubaneswar created a prototype to assess and show the efficiency and dependability of the imaging system under difficult environments like low light, dust, smoke or fog. Besides, the team sought to examine the potential applications of image processing in moving around in extreme terrain, finding objects and obstructions, and their size and form classification. Their research gives a useful understanding into image processing in these underground harsh environments..

[Audio] Researchers from C V Raman Global University in Bhubaneswar, India, namely Ruturaj Samantaray, Asim Swarup, Soham Rath, Ankur Kumari, and Jyoti Swain, are investigating the potential of image processing in extreme underground environments. For the first phase of the research, IoT sensors will be implemented to acquire data in adverse settings while LoRa sensor will be utilized for extended communication. During the subsequent stage, YOLO and HOG will be deployed for object recognition and detection..

[Audio] The research paper of Ruturaj Samantaray, Asim Swarup, Soham Rath, Ankur Kumari and Jyoti Swain from C V Raman Global University in Bhubaneswar, India focuses on image processing in underground harsh environments. They proposed a system which has high adaptability and uses LoRa sensor for long range and low-power communication, thus enabling object detection and classification with a range of 1-3 miles. Nonetheless, the research also revealed that the system relies on internet connectivity and computational complexity..

Technical Description. [image] • Enable • the devices in remte Type ot chan" with the of light it is camera (RGB) in Ultrasonic Sensor • mte • Coming for at a distm. Camera (Infrared) ot retying visible lint, IR Sensor object detectiM are adept identifying Microcontroller (Raspberry Pi) ESP8266 Module Fig. I: Technical description Of sensory system.

[Audio] This research paper addresses the application of image processing technology for underground harsh environment surveillance. It has been conducted by Ruturaj Samantaray, Asim Swarup, Soham Rath, Ankur Kumari and Jyoti Swain from C V Raman Global University in Bhubaneswar, India. They have innovatively used a deep learning-based object detection algorithm, known as "You Only Look Once" (YOLO). YOLO is remarkable in that it provides the capability to detect multiple objects within an environment in a single pass, as opposed to needing to conduct multiple passes with traditional technology. This technology has the potential to be invaluable for surveying underground environments, which are usually hostile and hazardous..

Technical Description. Fig. 3: Technical description of Histogram of Oriented Gradients.

[Audio] The researchers from C V Raman Global University conducted image processing, as demonstrated by Fig.4 and Fig.5, which are block diagrams of the sensory system and image processing respectively. This image processing was conducted in underground harsh environments and reveals the capabilities of modern technology. The results of this research will contribute to the development of better solutions to the difficulties faced in underground harsh environments..

Validation / Testing / Analysis. [image]. [image].

[Audio] Our research paper investigates the utilization of image processing in harsh underground environments. We focus on the research of Ruturaj Samantaray, Asim Swarup, Soham Rath, Ankur Kumari and Jyoti Swain from C V Raman Global University in Bhubaneswar, India. We assessed the varied approaches used to recognize and spot images in such settings. We found that these algorithms and methods are effective for recognizing features and objects in these kinds of conditions. Our paper gives important knowledge on how image processing can be applied in underground harsh environments..

Validation / Testing / Analysis. [image]. [image].

[Audio] This research paper discusses the use of image processing, conducted by Ruturaj Samantaray, Asim Swarup, Soham Rath, Ankur Kumari, and Jyoti Swain from C V Raman Global University in Bhubaneswar, India. The paper focuses on image processing in underground harsh environments, for which they have included various components such as a LoRa module for 2,821 rupees, a Raspberry Pi 3 for 4,499 rupees, an RGB camera for 4,097 rupees, an IR camera for 3,050 rupees, a light for 1,099 rupees, an Ultrasonic Sensor for 499 rupees and an IR Sensor for 387 rupees. By implementing these components, they have been able to achieve invaluable insights about their environment..

[Audio] This research paper presents a novel robotic system developed for image processing in underground harsh environments. The robotic system is capable of carrying a computer vision-based module into underground harsh environments and can establish a reliable internet connection for real-time image processing. Ruturaj Samantaray, Asim Swarup, Soham Rath, Ankur Kumari and Jyoti Swain from C V Raman Global University in Bhubaneswar, India conducted the research and developed the robotic system. This robotic system is designed to operate under extreme conditions and enable autonomous underground mapping..

[Audio] Underground harsh environment image processing for autonomous robotic vehicle navigation." In International Conference on Advances in Visual Computing, pp. 574-584. Springer, Berlin, Heidelberg, 2019. This research paper discusses a groundbreaking study conducted by Ruturaj Samantaray, Asim Swarup, Soham Rath, Ankur Kumari and Jyoti Swain from C V Raman Global University in Bhubaneswar, India. The paper examines the use of image processing to facilitate underground navigation for robots in harsh environments. This research explores the applications of infrared thermography, unmanned ground vehicles, LoRa and server-based home automation, laser scanning, and a transfer learning-based YOLO network for sewer defect detection for this purpose. In addition, it also investigates the use of a cascade of histograms of oriented gradients for fast human detection, ultrasonic radar for moving object detection, and image processing for autonomous robotic vehicle navigation. This study is an important contribution to understanding the utility of image processing and research for vehicles navigating harsh underground environments..

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