Real-Time UAV Trash Monitoring System

Abstract

This study proposes a marine trash detection system based on unmanned aerial vehicles (UAVs) and aims to replace manpower with UAVs to detect marine trash efficiently and provide information to government agencies regarding real-time trash pollution. Internet technology and computer–machine interaction were applied in this study, which involves the deployment of a marine trash detection system on a drone’s onboard computer for real-time calculations. Images of marine trash were provided to train a modified YOLO model (You Look Only Once networks). The UAV was shown to be able to fly along a predefined path and detect trash in coastal areas. The detection results were sent to a data streaming platform for data processing and analysis. The Kafka message queuing system and the Mongo database were used for data transmission and analysis. It was shown that a real-time drone map monitoring station can be built up at any place where mobile communication is accessible. While a UAV is automatically controlled by an onboard computer, it can also be controlled through a remote station. It was shown that the proposed system can perform data analysis and transmit heatmaps of coastal trash information to a remote site. From the heatmaps, government agencies can use trash categories and locations to take further action.

Сучасні Кораблі Та Інтеграція Безпілотників – Нова Ера Для Морських Комунікацій

“…Finally, analyzed the level of technical preparedness of contemporary vessels for drone integration. They concluded that although progress had been achieved, there was still a need for improvement in areas like drone landing pads, navigational systems, and shipboard protocols for managing drones [12].…”

Deep Learning Model Analysis of Drone Images for Unauthorized Occupancy Detection of River Site

“…The GeoAI dataset (Ballesteros, Sanchez-Torres, and Branch-Bedoya, 2022) was used to train object detection and semantic segmentation models for geospatial data analysis. Liao and Juang (2022) proposed a monitoring system that uses drones in real-time to detect waste on beaches and in the ocean. Other studies have modified loss functions in YOLOv3 to detect waste using dronebased systems (Niu et al, 2019;Redmon and Farhadi, 2018).…”

A Study on the Implementation of YOLOv4 Algorithm with Hyperparameter Tuning for Car Detection in Unmanned Aerial Vehicle Images

“…Several studies have been conducted regarding YOLO. First, research was conducted by Liao et al [6] entitled Real-Time UAV Trash Monitoring System. The main objective of his study is to build UAV and IoT architectures using the YOLOv4-Tiny-3l model, which is deployed to embedded systems so that UAVs have the mobility to obtain beach images and map garbage information in real-time for further analysis [6].…”

“…First, research was conducted by Liao et al [6] entitled Real-Time UAV Trash Monitoring System. The main objective of his study is to build UAV and IoT architectures using the YOLOv4-Tiny-3l model, which is deployed to embedded systems so that UAVs have the mobility to obtain beach images and map garbage information in real-time for further analysis [6]. The advantages of this research are hyperparameter adjustment and model evaluation to get the best model and the lowest generalization error.…”