Improved Target Detection Algorithm for Aerial Images Based on YOLOv5

Authors

Yecai Guo, School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; School of Electronic Information Engineering, Wuxi University, Wuxi 214105, China
Jingdong Sun, School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Saha Amitave, School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

Abstract

Abstract: In order to improve the existing small target detection methods, which suffer from low detection accuracy, high false detection rate and high leakage rate, the FSD-YOLOv5 algorithm is proposed, which has three improvements based on the YOLOv5 algorithm. The Focal EIoU is used instead of the original CIoU to improve the model convergence speed and regression accuracy. To cope with the deficiencies in CNN architecture, we adopt a new CNN building block called SPD-Conv is adopted. To address the problem of the reduced or lost information of small objects in feature maps caused by downsampling in convolutional neural networks, feature reuse is introduced to increase the feature information of small objects in the feature maps. Experimental results show that FSD-YOLOv5 achieves a detection accuracy of 36.3%, an improvement of 2.4% in comparison with original algorithm.

Recommended Citation

Guo, Yecai; Sun, Jingdong; and Amitave, Saha (2025) "Improved Target Detection Algorithm for Aerial Images Based on YOLOv5," Journal of System Simulation: Vol. 37: Iss. 2, Article 21.
DOI: 10.16182/j.issn1004731x.joss.23-1564
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss2/21

First Page

551

Last Page

562

CLC

TP39

Recommended Citation

Guo Yecai, Sun Jingdong, Amitave Saha . Improved Target Detection Algorithm for Aerial Images Based on YOLOv5[J]. Journal of System Simulation, 2025, 37(2): 551-562.

DOI

10.16182/j.issn1004731x.joss.23-1564