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VPP: Visual Pollution Prediction Framework Based on a Deep Active Learning Approach Using Public Road Images

Author

Listed:
  • Mohammad AlElaiwi

    (Computer Science Department, College of Computer Sciences and Information Technology (CCSIT), King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia)

  • Mugahed A. Al-antari

    (Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea)

  • Hafiz Farooq Ahmad

    (Computer Science Department, College of Computer Sciences and Information Technology (CCSIT), King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia)

  • Areeba Azhar

    (Department of Mathematics, College of Natural & Agricultural Sciences, University of California-Riverside (UCR), Riverside, CA 92521, USA)

  • Badar Almarri

    (Computer Science Department, College of Computer Sciences and Information Technology (CCSIT), King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia)

  • Jamil Hussain

    (Department of Data Science, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea)

Abstract

Visual pollution (VP) is the deterioration or disruption of natural and man-made landscapes that ruins the aesthetic appeal of an area. It also refers to physical elements that limit the movability of people on public roads, such as excavation barriers, potholes, and dilapidated sidewalks. In this paper, an end-to-end visual pollution prediction (VPP) framework based on a deep active learning (DAL) approach is proposed to simultaneously detect and classify visual pollutants from whole public road images. The proposed framework is architected around the following steps: real VP dataset collection, pre-processing, a DAL approach for automatic data annotation, data splitting as well as augmentation, and simultaneous VP detection and classification. This framework is designed to predict VP localization and classify it into three categories: excavation barriers, potholes, and dilapidated sidewalks. A real dataset with 34,460 VP images was collected from various regions across the Kingdom of Saudi Arabia (KSA) via the Ministry of Municipal and Rural Affairs and Housing (MOMRAH), and this was used to develop and fine-tune the proposed artificial intelligence (AI) framework via the use of five AI predictors: MobileNetSSDv2, EfficientDet, Faster RCNN, Detectron2, and YOLO. The proposed VPP-based YOLO framework outperforms competitor AI predictors with superior prediction performance at 89 % precision, 88% recall, 89% F1-score, and 93% mAP. The DAL approach plays a crucial role in automatically annotating the VP images and supporting the VPP framework to improve prediction performance by 18% precision, 27% recall, and 25% mAP. The proposed VPP framework is able to simultaneously detect and classify distinct visual pollutants from annotated images via the DAL strategy. This technique is applicable for real-time monitoring applications.

Suggested Citation

  • Mohammad AlElaiwi & Mugahed A. Al-antari & Hafiz Farooq Ahmad & Areeba Azhar & Badar Almarri & Jamil Hussain, 2022. "VPP: Visual Pollution Prediction Framework Based on a Deep Active Learning Approach Using Public Road Images," Mathematics, MDPI, vol. 11(1), pages 1-26, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:186-:d:1019444
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    References listed on IDEAS

    as
    1. Wang, Xuechao & Chen, Jinzhou & Quan, Shengwei & Wang, Ya-Xiong & He, Hongwen, 2020. "Hierarchical model predictive control via deep learning vehicle speed predictions for oxygen stoichiometry regulation of fuel cells," Applied Energy, Elsevier, vol. 276(C).
    2. Khydija Wakil & Malik Asghar Naeem & Ghulam Abbas Anjum & Abdul Waheed & Muhammad Jamaluddin Thaheem & Muhammad Qadeer ul Hussnain & Raheel Nawaz, 2019. "A Hybrid Tool for Visual Pollution Assessment in Urban Environments," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    3. Szymon Chmielewski, 2020. "Chaos in Motion: Measuring Visual Pollution with Tangential View Landscape Metrics," Land, MDPI, vol. 9(12), pages 1-21, December.
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