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Biomimetic Vision for Zoom Object Detection Based on Improved Vertical Grid Number YOLO Algorithm.

Xinyi Shen1, Guolong Shi1,2, Huan Ren1

  • 1School of Information and Computer, Anhui Agricultural University, Hefei, China.

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Summary
This summary is machine-generated.

This study enhances the YOLO algorithm for improved target detection in high-resolution zoom sensing images. The improved algorithm offers higher accuracy and real-time performance for remote sensing applications.

Keywords:
bionic visiondeep learningimage segmentationlight/dark co-occurrence scenesimple linear iterative clusteringzoom target detection

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Area of Science:

  • Computer Vision
  • Remote Sensing
  • Image Processing

Background:

  • High-resolution zoom sensing images are crucial for applications like drone-based surveillance and environmental monitoring.
  • Image segmentation and target detection are vital steps in processing these images, but are often hindered by blur and distortion.
  • Existing algorithms struggle with accurately detecting targets in remote sensing images due to variations in lighting and contour boundaries.

Purpose of the Study:

  • To improve the accuracy and real-time performance of target detection in high-resolution zoom sensing images.
  • To address challenges posed by image distortions and lighting variations in remote sensing data.
  • To develop an enhanced YOLO algorithm tailored for the specific characteristics of zoom sensing imagery.

Main Methods:

  • Utilized grey-level co-occurrence matrix to extract texture features and Simple Linear Iterative Clustering (SLIC) for light/dark scene segmentation.
  • Developed a high-resolution zoom sensing image model to standardize datasets for recognition.
  • Proposed an improved YOLO algorithm by adjusting the vertical grid number within its network structure to better suit target aspect ratios.

Main Results:

  • The enhanced YOLO algorithm demonstrated superior accuracy compared to the standard YOLO algorithm in target detection tasks.
  • The algorithm achieved real-time performance, making it suitable for time-sensitive remote sensing applications.
  • Quantitative analysis confirmed the algorithm's effectiveness in handling "short and coarse" objects within high information density images.

Conclusions:

  • The improved YOLO algorithm offers a significant advancement for target detection in high-resolution zoom sensing images.
  • The method effectively mitigates issues related to image distortion and lighting, enhancing detection reliability.
  • This research provides a robust solution for real-time, accurate object identification in complex remote sensing scenarios.