IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i15p2650-d874430.html
   My bibliography  Save this article

An Improved Soft-YOLOX for Garbage Quantity Identification

Author

Listed:
  • Junran Lin

    (School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China)

  • Cuimei Yang

    (School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China)

  • Yi Lu

    (School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China)

  • Yuxing Cai

    (School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China)

  • Hanjie Zhan

    (School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China)

  • Zhen Zhang

    (School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China)

Abstract

Urban waterlogging is mainly caused by garbage clogging the sewer manhole covers. If the amount of garbage at a sewer manhole cover can be detected, together with an early warning signal when the amount is large enough, it will be of great significance in preventing urban waterlogging from occurring. Based on the YOLOX algorithm, this paper accomplishes identifying manhole covers and garbage and building a flood control system that can automatically recognize and monitor the accumulation of garbage. This system can also display the statistical results and send early warning information. During garbage identification, it can lead to inaccurate counting and a missed detection if the garbage is occluded. To reduce the occurrence of missed detections as much as possible and improve the performance of detection models, Soft-YOLOX, a method using a new detection model for counting, was used as it can prevent the occurrence of missed detections by reducing the scores of adjacent detection frames reasonably. The Soft-YOLOX improves the accuracy of garbage counting. Compared with the traditional YOLOX, the mAP value of Soft-YOLOX for garbage identification increased from 89.72% to 91.89%.

Suggested Citation

  • Junran Lin & Cuimei Yang & Yi Lu & Yuxing Cai & Hanjie Zhan & Zhen Zhang, 2022. "An Improved Soft-YOLOX for Garbage Quantity Identification," Mathematics, MDPI, vol. 10(15), pages 1-12, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2650-:d:874430
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/15/2650/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/15/2650/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei, Yun & Tian, Qing & Guo, Jianhua & Huang, Wei & Cao, Jinde, 2019. "Multi-vehicle detection algorithm through combining Harr and HOG features," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 130-145.
    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. Aleksey Osipov & Ekaterina Pleshakova & Sergey Gataullin & Sergey Korchagin & Mikhail Ivanov & Anton Finogeev & Vibhash Yadav, 2022. "Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions," Sustainability, MDPI, vol. 14(4), pages 1-16, February.
    2. Jing, Shaoxue, 2023. "Time-delay Hammerstein system identification using modified cross-correlation method and variable stacking length multi-error algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 288-300.

    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:jmathe:v:10:y:2022:i:15:p:2650-:d:874430. 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.