Autonomous Amphibious Vehicle for Monitoring and Collecting Marine Debris

Patil, Chinmay; Tanpure, Sumit; Lohiya, Ankit; Pawar, Swaraj; Mohite, Pratiksha

doi:10.1109/icrae50850.2020.9310888

Cited by 3 publications

(1 citation statement)

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“…Chest abnormality is detected based on YOLOv5 model by using ResNet50 controller [12]. A vehicle is designed to float on the [13]. Various technologies use one you look family (YOLO) algorithms based on machine and deep learning to detect smoke early in forests satellite imagery [14].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation monitoring robotic system based on you only look once model using deep learning technique

Al-Tameemi

Hasan²,

Oleiwi³

2023

IJ-AI

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<span lang="EN-US">The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. The main goal of this work is to design and implement a rover robotic monitoring system based on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using you only look once algorithm-version five (YOLOv5) to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in undesirable places to transmit live video with a moving camera and process it by the YOLOv5 model. Also, the robot system can receive images, videos, or YouTube links and process them with YOLOv5. Raspberry Pi is controlled remotely by connecting to the network through Wi-Fi locally or publicly using the internet with a remote desktop connection application. The results were very satisfactory and proved the high-performance efficiency of the robot.</span>

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Section: Introductionmentioning

confidence: 99%