Safe Construction And Visual Pollution Of Billboards Along Main Street

Andjarsari, Sophia; Subadyo, A. Tutut; Bonifacius, Nurhamdoko

doi:10.1088/1755-1315/999/1/012015

Cited by 3 publications

(1 citation statement)

References 11 publications

(28 reference statements)

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“…The implemented customized CNN model attained a training accuracy of 95% and a validation accuracy of 85%. Andjarsari and her team [16] reported that the presence of billboards and street graffiti along the route could impact the area's aesthetics and potentially obstruct vision. To investigate visual pollution, the authors employed an AHP-based SBE technique.…”

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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From Above and Beyond: Decoding Urban Aesthetics with the Visual Pollution Index

Gupta,

Padsala,

Jani

et al. 2024

Studies in Computational Intelligence

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A Systematic Literature Review and Analysis of Visual Pollution

Gao,

Bakar,

Maulan

et al. 2024

Land

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Rapid urbanization has introduced new pollution challenges, with visual pollution becoming particularly prominent. This type of pollution affects both the visual environment and public psychology, impairing aesthetic appreciation. Visual pollution extends beyond outdoor advertising, manifesting in various forms across urban, roadway, and natural areas. Although many studies have identified and analyzed visual pollution, there is still a lack of comprehensive knowledge and awareness of this problem. Until now, visual pollution has never been a unified and complete concept, definition, and research methodology. To address this gap, our systematic literature review examined existing literature to further explore and understand visual pollution. We systematically reviewed research articles published between 2008 and 2023, utilizing three journal databases: Web of Science, Scopus, and Google Scholar. Ultimately, 52 articles met the review criteria. The results of the study showed the types and characteristics of visual pollutants, the locations where visual pollution occurs, the various factors contributing to visual pollution, and the methodologies employed to study visual pollution. This study enhances professionals’ comprehension of visual pollution and its effects on the visual environment, equipping them to implement effective measures to reduce its impact and preserve visual quality in both urban and natural areas.

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Safe Construction And Visual Pollution Of Billboards Along Main Street

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

From Above and Beyond: Decoding Urban Aesthetics with the Visual Pollution Index

A Systematic Literature Review and Analysis of Visual Pollution

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