Visual Pollution Detection Using Google Street View and YOLO

Hossain, Md. Yearat; Nijhum, Ifran Rahman; Sadi, Abu Adnan; Shad, Md. Tazin Morshed; Rahman, Rashedur M.

doi:10.1109/uemcon53757.2021.9666654

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Hossain et al [17] introduced artificial intelligence techniques for identifying visual contaminants using images from Google Street View. The authors selected the different roads of Dhaka, the capital city of Bangladesh, as their test subject due to its recent ranking as one of the world's most polluted cities.…”

Section: Related Workmentioning

confidence: 99%

Deep-Learning-Based Real-Time Visual Pollution Detection in Urban and Textile Environments

Titu,

Chowdhury,

Haque

et al. 2024

Sci

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The environmental physiognomy of an area can significantly diminish its aesthetic appeal, rendering it susceptible to visual pollution, the unbeaten scourge of modern urbanization. In this study, we propose using a deep learning network and a robotic vision system integrated with Google Street View to identify streets and textile-based visual pollution in Dhaka, the megacity of Bangladesh. The issue of visual pollution extends to the global apparel and textile industry, as well as to various common urban elements such as billboards, bricks, construction materials, street litter, communication towers, and entangled electric wires. Our data collection encompasses a wide array of visual pollution elements, including images of towers, cables, construction materials, street litter, cloth dumps, dyeing materials, and bricks. We employ two open-source tools to prepare and label our dataset: LabelImg and Roboflow. We develop multiple neural network models to swiftly and accurately identify and classify visual pollutants in this work, including Faster SegFormer, YOLOv5, YOLOv7, and EfficientDet. The tuna swarm optimization technique has been used to select the applied models’ final layers and corresponding hyperparameters. In terms of hardware, our proposed system comprises a Xiaomi-CMSXJ22A web camera, a 3.5-inch touchscreen display, and a Raspberry Pi 4B microcontroller. Subsequently, we program the microcontroller with the YOLOv5 model. Rigorous testing and trials are conducted on these deep learning models to evaluate their performance against various metrics, including accuracy, recall, regularization and classification losses, mAP, precision, and more. The proposed system for detecting and categorizing visual pollution within the textile industry and urban environments has achieved notable results. Notably, the YOLOv5 and YOLOv7 models achieved 98% and 92% detection accuracies, respectively. Finally, the YOLOv5 technique has been deployed into the Raspberry Pi edge device for instantaneous visual pollution detection. The proposed visual pollutants detection device can be easily mounted on various platforms (like vehicles or drones) and deployed in different urban environments for on-site, real-time monitoring. This mobility is crucial for comprehensive street-level data collection, potentially engaging local communities, schools, and universities in understanding and participating in environmental monitoring efforts. The comprehensive dataset on visual pollution will be published in the journal following the acceptance of our manuscript.

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Section: Related Workmentioning

confidence: 99%

Deep-Learning-Based Real-Time Visual Pollution Detection in Urban and Textile Environments

Titu,

Chowdhury,

Haque

et al. 2024

Sci

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“…While noise pollution has been extensively studied (e.g., [13][14][15][16][17]), this is not the case for visual pollution. "Visual pollutants" [18] may refer to advertisements, signage, and litter, but also any other element-both indoor and outdoor-that an observer finds unpleasant or offensive to look at. They also degrade the visual quality of a place [19].…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning was used to classify and measure the visual pollution in Bangladesh [19], providing information about if there was visual pollution in a picture. Another similar approach was adopted in [18] using Google Maps tools and You Only Look Once (YOLO) methodologies. They claim that if the location of each photograph is captured, a map can be constructed with the type and significance of visual pollution present in each geographical zone.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Visual Pollution from Urban Air Mobility

Thomas

Granberg

2023

Drones

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Unmanned aerial vehicles (UAVs) can bring many benefits, particularly in emergency response and disaster management. However, they also induce negative effects, such as noise and visual pollution, risk, and integrity concerns. In this work, we study visual pollution, developing a quantitative measure that can calculate the visual pollution from one or multiple UAVs. First, the Analytic Hierarchy Process was utilized in an expert workshop to find and rank factors relevant to visual pollution. Then an image-based questionnaire targeted at the general public was used to find relations between the factors. The results show that the two main factors causing visual pollution are the number of UAVs and the distance between a UAV and the observer. They also show that while a UAV used for emergency medical services is as polluting as any other UAV, it is easier to tolerate this pollution. Based on the questionnaire results, two visual pollution functions were developed that can be used when carrying out path planning for one or multiple UAVs. When combining this function with other existing measures for noise pollution, and ground and air risk, it is possible to find paths that will give as little negative impact as possible from urban air mobility.

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2022

Visual Pollution

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Visual Pollution Detection Using Google Street View and YOLO

Cited by 3 publications

References 11 publications

Deep-Learning-Based Real-Time Visual Pollution Detection in Urban and Textile Environments

Deep-Learning-Based Real-Time Visual Pollution Detection in Urban and Textile Environments

Quantifying Visual Pollution from Urban Air Mobility

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