Autonomous UAV forced graffiti detection and removal system based on machine learning

Nahar, Prakhar; Wu, Kang-hua; Mei, Sitao; Ghoghari, Hadiyah; Srinivasan, Preethi; Lee, Yueh-lin; Gao, Jerry; Guan, Xuan

doi:10.1109/uic-atc.2017.8397582

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…Wang et al [205] surveyed a ML approach, with SSD and MobileNet, for graffiti detection and removal with UAVs. Nahar et al [206] presented an intelligent graffiti cleaning system based on edge detection and ML algorithms to perform real-time detection of graffiti in images. Masuduzzaman et al [207] propose an automated and secure garbage management scheme using Unmanned any Vehicle (UxV) integrated with DL models for garbage detection.…”

Section: • Cleaningmentioning

confidence: 99%

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Teixeira,

Miguel,

Silva

et al. 2023

IEEE Access

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Unmanned Aerial Vehicles (UAVs) play an important role in many applications, including health, transport, telecommunications and safe and rescue operations. Their adoption can improve the speed and precision of applications when compared to traditional solutions based on handwork. The use of UAVs brings scientific and technological challenges. In this context, Machine Learning (ML) techniques provide solutions to several problems concerning the use of UAVs in civil and military applications. An increasing number of scientific papers on the use of ML in UAVs context have been published in academic journals. In this work, we present a literature review on the use of ML techniques in UAVs, outlining the most recurrent areas and the most commonly used ML techniques in UAV applications. The results reveal that applications in the areas of environment, communication and security are among the main research topics.INDEX TERMS unmanned aerial vehicle, machine learning, literature review, UAV applications, neural networks.

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Section: • Cleaningmentioning

confidence: 99%

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Teixeira,

Miguel,

Silva

et al. 2023

IEEE Access

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“…Nahar et al [13] proposed a system based on an autonomous Unmanned Aerial Vehicle (UAV) that can detect graffitied walls and cover them with spray paint if necessary. This was designed to clean hard-to-reach public places such as bridges and highways.…”

Section: Related Workmentioning

confidence: 99%

“…Sci. 2023, 13,2249 Since the model is trained based on the classes of these images, it is e there will be some misclassifications in some cases, as in Figure 16 (left image ages, such as those in Figure 16 (central and right image), contain street art e illegal graffiti. However, since they were images taken from the Lisbon urban they were classified as street art.…”

Section: Illegal Graffiti Detectormentioning

confidence: 99%

Deep Learning-Based Graffiti Detection: A Study Using Images from the Streets of Lisbon

2023

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This research work comes from a real problem from Lisbon City Council that was interested in developing a system that automatically detects in real-time illegal graffiti present throughout the city of Lisbon by using cars equipped with cameras. This system would allow a more efficient and faster identification and clean-up of the illegal graffiti constantly being produced, with a georeferenced position. We contribute also a city graffiti database to share among the scientific community. Images were provided and collected from different sources that included illegal graffiti, images with graffiti considered street art, and images without graffiti. A pipeline was then developed that, first, classifies the image with one of the following labels: illegal graffiti, street art, or no graffiti. Then, if it is illegal graffiti, another model was trained to detect the coordinates of graffiti on an image. Pre-processing, data augmentation, and transfer learning techniques were used to train the models. Regarding the classification model, an overall accuracy of 81.4% and F1-scores of 86%, 81%, and 66% were obtained for the classes of street art, illegal graffiti, and image without graffiti, respectively. As for the graffiti detection model, an Intersection over Union (IoU) of 70.3% was obtained for the test set.

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“…This method is Versatile Defacement Detection by Monitoring Video Sequences Using Deep Learning R. Newlin Shebiah, and S. Arivazhagan mostly applicable for graffiti detection in bus stops. The overall objective of Prabhakar et.al [5] is to prevent closing highways for cleaning up graffiti. This paper proposes a smart graffiti clean-up system based on an autonomous Unmanned Aerial Vehicle (UAV) platform.…”

Section: Introductionmentioning

confidence: 99%

Versatile Defacement Detection by Monitoring Video Sequences Using Deep Learning

R¹,

Arivazhagan²

2019

EJERS

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The main objective of this paper is to detect vandal and vandalism by monitoring recorded video sequences. Vandalism is one of the most commonly occurring crimes in the society that indirectly affects the economy of the country. The proposed algorithm takes in the input from the video extracted from surveillance camera which prevails in public places. Further, it is converted into frames and subtracted with the background to detect the foreground object. The background subtracted image contains both human and non-human moving objects. In order to differentiate human pixels and other moving objects in the video sequence, discriminative features are extracted using deep architecture and classified using SVM classifier. Deep features proved to be highly discriminative when compared with the handcrafted Histogram of Oriented Gradients features. By analyzing the dwell time of the person in the restricted scene and his motion pattern with time and significant change in background vandalism act is declared and the person is considered as vandal. The proposed method was evaluated on the videos collected from You Tube with the contents taken during night time, multiple vandals, car vandals etc.

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Autonomous UAV forced graffiti detection and removal system based on machine learning

Cited by 9 publications

References 5 publications

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Deep Learning-Based Graffiti Detection: A Study Using Images from the Streets of Lisbon

Versatile Defacement Detection by Monitoring Video Sequences Using Deep Learning

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