IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2023i1p117-d1305224.html
   My bibliography  Save this article

CNN with New Spatial Pyramid Pooling and Advanced Filter-Based Techniques: Revolutionizing Traffic Monitoring via Aerial Images

Author

Listed:
  • Irfan Javid

    (Department of Computer Science and IT, University of Poonch, Rawalakot 12350, Pakistan)

  • Rozaida Ghazali

    (Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia)

  • Waddah Saeed

    (School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK)

  • Tuba Batool

    (Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia)

  • Ebrahim Al-Wajih

    (Society Development & Continuing Education Center, Hodeidah University, Hodeidah P.O. Box 3114, Yemen)

Abstract

The escalation in vehicular traffic, in conjunction with global population growth, has precipitated heightened road congestion, augmented air pollution, and a rise in vehicular accidents. Over the past decade, the global vehicular count has witnessed a substantial surge. In this context, traffic monitoring emerges as a paramount challenge, especially within developing nations. This research introduces an innovative system for vehicle detection and categorization aimed at intelligent traffic monitoring. The system utilizes a convolutional neural network-based U-Net model for the segmentation of aerial images. After segmentation, the outputs are further analyzed for vehicle identification. This vehicle detection utilizes an advanced spatial pyramid pooling (ASPP) mechanism which refines the spatial partitions of the image and captures intricate details, enhancing the accuracy and precision of the detection process. Detected vehicles are then categorized into distinct subcategories. For the effective management and control of high-density traffic flow, the extended Kalman filter (EKF) technique is employed, thereby reducing the reliance on human oversight. In experimental evaluations, our proposed model exhibits exemplary vehicle detection capabilities across the German Aerospace Center (DLR3K) and the Vehicle Detection in Aerial Imagery (VEDAI) datasets. Potential applications of the system encompass vehicle identification in traffic streams, traffic congestion assessment, intersection traffic density analysis, differentiation of vehicle types, and pedestrian pathway determination.

Suggested Citation

  • Irfan Javid & Rozaida Ghazali & Waddah Saeed & Tuba Batool & Ebrahim Al-Wajih, 2023. "CNN with New Spatial Pyramid Pooling and Advanced Filter-Based Techniques: Revolutionizing Traffic Monitoring via Aerial Images," Sustainability, MDPI, vol. 16(1), pages 1-13, December.
  • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:117-:d:1305224
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/1/117/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/1/117/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Jinlong & Huang, Qiao & Ulishney, Christopher & Dumitrescu, Cosmin E., 2021. "Machine learning assisted prediction of exhaust gas temperature of a heavy-duty natural gas spark ignition engine," Applied Energy, Elsevier, vol. 300(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chang, Mengzhao & Park, Suhan, 2023. "Predictions and analysis of flash boiling spray characteristics of gasoline direct injection injectors based on optimized machine learning algorithm," Energy, Elsevier, vol. 262(PA).
    2. Zhou, Mengmeng & Wang, Shuai & Luo, Kun & Fan, Jianren, 2022. "Three-dimensional modeling study of the oxy-fuel co-firing of coal and biomass in a bubbling fluidized bed," Energy, Elsevier, vol. 247(C).
    3. Yuan, Chenheng & Peng, Shizhuo & Zhou, Lifu, 2023. "Multi-field coupling effect of injection on dynamics and thermodynamics of a linear combustion engine generator with slow compression and fast expansion," Energy, Elsevier, vol. 270(C).
    4. Farhan, Muhammad & Chen, Tianhao & Rao, Anas & Shahid, Muhammad Ihsan & Xiao, Qiuhong & Liu, Yongzheng & Ma, Fanhua, 2024. "Performance, emissions and combustion analysis of hydrogen-enriched compressed natural gas spark ignition engine by optimized Gaussian process regression and neural network at low speed on different l," Energy, Elsevier, vol. 302(C).
    5. Ruomiao Yang & Tianfang Xie & Zhentao Liu, 2022. "The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines," Energies, MDPI, vol. 15(9), pages 1-16, April.
    6. Pinyi Su & Muhammad Imran & Muhammad Nadeem & Shamsheer ul Haq, 2023. "The Role of Environmental Law in Farmers’ Environment-Protecting Intentions and Behavior Based on Their Legal Cognition: A Case Study of Jiangxi Province, China," Sustainability, MDPI, vol. 15(11), pages 1-22, May.
    7. Cesar de Lima Nogueira, Silvio & Och, Stephan Hennings & Moura, Luis Mauro & Domingues, Eric & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2023. "Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering," Energy, Elsevier, vol. 280(C).
    8. Okeleye, Samuel Adeola & Thiruvengadam, Arvind & Perhinschi, Mario G. & Carder, Daniel, 2024. "Data-driven machine learning model of a Selective Catalytic Reduction on Filter (SCRF) in a heavy-duty diesel engine: A comparison of Artificial Neural Network with Tree-based algorithms," Energy, Elsevier, vol. 290(C).
    9. Wang, Huaiyu & Ji, Changwei & Shi, Cheng & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan & Chang, Ke & Meng, Hao & Wang, Xin, 2023. "Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm," Energy, Elsevier, vol. 263(PD).
    10. Farhan, Muhammad & Chen, Tianhao & Rao, Anas & Shahid, Muhammad Ihsan & Liu, Yongzheng & Ma, Fanhua, 2024. "Comparative knock analysis of HCNG fueled spark ignition engine using different heat transfer models and prediction of knock intensity by artificial neural network fitting tool," Energy, Elsevier, vol. 304(C).
    11. Wang, Yuhua & Wang, Guiyong & Yao, Guozhong & Shen, Qianqiao & Yu, Xuan & He, Shuchao, 2023. "Combining GA-SVM and NSGA-â…˘ multi-objective optimization to reduce the emission and fuel consumption of high-pressure common-rail diesel engine," Energy, Elsevier, vol. 278(PA).
    12. Yuan, Chenheng & He, Lei & Zhou, Lifu, 2022. "Numerical simulation of the effect of spring dynamics on the combustion of free piston linear engine," Energy, Elsevier, vol. 254(PA).
    13. Cao, Jiale & Li, Tie & Huang, Shuai & Chen, Run & Li, Shiyan & Kuang, Min & Yang, Rundai & Huang, Yating, 2023. "Co-optimization of miller degree and geometric compression ratio of a large-bore natural gas generator engine with novel Knock models and machine learning," Applied Energy, Elsevier, vol. 352(C).
    14. Wang, Xin & Liu, Xiang & Bai, Yun, 2024. "Prediction of the temperature of diesel engine oil in railroad locomotives using compressed information-based data fusion method with attention-enhanced CNN-LSTM," Applied Energy, Elsevier, vol. 367(C).
    15. Fei, Mingda & Zhang, Zhenyu & Zhao, Wenbo & Zhang, Peng & Xing, Zhaolin, 2024. "Optimal power distribution control in modular power architecture using hydraulic free piston engines," Applied Energy, Elsevier, vol. 358(C).
    16. Sok, Ratnak & Jeyamoorthy, Arravind & Kusaka, Jin, 2024. "Novel virtual sensors development based on machine learning combined with convolutional neural-network image processing-translation for feedback control systems of internal combustion engines," Applied Energy, Elsevier, vol. 365(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:117-:d:1305224. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.