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Soft-NMS-Enabled YOLOv5 with SIOU for Small Water Surface Floater Detection in UAV-Captured Images

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  • Fuxun Chen

    (College of The Academy of Digital China, Fuzhou University, Fuzhou 350003, China)

  • Lanxin Zhang

    (College of The Academy of Digital China, Fuzhou University, Fuzhou 350003, China)

  • Siyu Kang

    (The College of Computer and Data Science, Fuzhou University, Fuzhou 350003, China)

  • Lutong Chen

    (The College of Computer and Data Science, Fuzhou University, Fuzhou 350003, China)

  • Honghong Dong

    (College of The Academy of Digital China, Fuzhou University, Fuzhou 350003, China)

  • Dan Li

    (College of The Academy of Digital China, Fuzhou University, Fuzhou 350003, China)

  • Xiaozhu Wu

    (College of The Academy of Digital China, Fuzhou University, Fuzhou 350003, China
    The College of Computer and Data Science, Fuzhou University, Fuzhou 350003, China)

Abstract

In recent years, the protection and management of water environments have garnered heightened attention due to their critical importance. Detection of small objects in unmanned aerial vehicle (UAV) images remains a persistent challenge due to the limited pixel values and interference from background noise. To address this challenge, this paper proposes an integrated object detection approach that utilizes an improved YOLOv5 model for real-time detection of small water surface floaters. The proposed improved YOLOv5 model effectively detects small objects by better integrating shallow and deep features and addressing the issue of missed detections and, therefore, aligns with the characteristics of the water surface floater dataset. Our proposed model has demonstrated significant improvements in detecting small water surface floaters when compared to previous studies. Specifically, the average precision (AP), recall (R), and frames per second (FPS) of our model achieved 86.3%, 79.4%, and 92%, respectively. Furthermore, when compared to the original YOLOv5 model, our model exhibits a notable increase in both AP and R, with improvements of 5% and 6.1%, respectively. As such, the proposed improved YOLOv5 model is well-suited for the real-time detection of small objects on the water’s surface. Therefore, this method will be essential for large-scale, high-precision, and intelligent water surface floater monitoring.

Suggested Citation

  • Fuxun Chen & Lanxin Zhang & Siyu Kang & Lutong Chen & Honghong Dong & Dan Li & Xiaozhu Wu, 2023. "Soft-NMS-Enabled YOLOv5 with SIOU for Small Water Surface Floater Detection in UAV-Captured Images," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10751-:d:1189705
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    References listed on IDEAS

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    1. Mohamed Benghanem & Adel Mellit & Chourouk Moussaoui, 2023. "Embedded Hybrid Model (CNN–ML) for Fault Diagnosis of Photovoltaic Modules Using Thermographic Images," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    2. Nobuo Imai & Hinata Otokawa & Atsumi Okamoto & Kaito Yamazaki & Takuya Tamura & Tsubasa Sakagami & Shingo Ishizaka & Hijiri Shimojima, 2023. "Abandonment of Cropland and Seminatural Grassland in a Mountainous Traditional Agricultural Landscape in Japan," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    3. Naresh Suwal & Alban Kuriqi & Xianfeng Huang & João Delgado & Dariusz Młyński & Andrzej Walega, 2020. "Environmental Flows Assessment in Nepal: The Case of Kaligandaki River," Sustainability, MDPI, vol. 12(21), pages 1-23, October.
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