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November 1, 2025Journal of Computational Methods in Sciences and Engineering

Improved YOLO v5 with principal component analysis for river object detection

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Authors

YZYan Zhai

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Overview

This study improves object detection in polluted rivers using an enhanced YOLO v5 model with principal component analysis for effective supervision.

Key Points

  • Improved object detection is achieved using a YOLO v5 model with principal component analysis features, enhancing performance in river management.
  • Results indicate a significant object detection enhancement with intersection over union ratios reaching up to 0.98 for the proposed method.
  • The approach integrates an attention mechanism, optimizing detection of floating objects while addressing river pollution and management needs.
  • This model has potential applications in effective river supervision and may contribute to better environmental conditions.

Cite This Study

Yan Zhai (2025) studied this question.

synapsesocial.com/papers/69054ffa1a99e50463de6846https://doi.org/10.1177/14727978251361843
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1AK-YOLOv9: Improved Object Detection for Pollutants in Rivers Discharging into the Sea2025
  2. 2Development of a Lightweight Floating Object Detection Algorithm2024 · 1 citations
  3. 3Multi-Objective Detection of River and Lake Spaces Based on YOLOv11n2026
  4. 4Low visibility underwater biological target detection based on YOLOV5s2024 · 1 citations
  5. 5Research on Real-Time Detection Method for River Floating Objects Using UAVs Based on Lightweight YOLOv8s Model2025