Enhancing Small Object Detection in Remote Sensing Images Using Mixed Local Channel Attention with YOLOv8

Hao Wang; Sergey Ablameyko

doi:10.5281/zenodo.10986298

Authors

Hao Wang Belarusian State University
Sergey Ablameyko Belarusian State University

DOI:

https://doi.org/10.5281/zenodo.10986298

References:

8

Keywords:

YOLO, Small Object Detection, Mixed Local Channel Attention, MLCA

Abstract

Small object detection is very popular in computer vision, and the attention mechanism can automatically learn and selectively focus on important information in the input, improving the performance and generalization ability of the model. This paper proposes a new algorithm based on combination of YOLOv8 and Mixed Local Channel Attention (MLCA) to detect small objects. The results show that YOLOv8 using Mixed Local Channel Attention performs better than using other attention mechanisms and the original YOLOv8.

Author Biographies

Hao Wang, Belarusian State University

Dept. of Mechanics and Mathematics Belarusian State University, Minsk, Belarus ahcenewang@gmail.com.

Sergey Ablameyko, Belarusian State University

Dept. of Mechanics and Mathematics Belarusian State University, Minsk, Belarus ablameyko@bsu.by.

References

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