Detection of Small Apple Targets Based on Improved YOLOv5 in Natural Environments

Authors

Zilong Liu, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Lei Zhang, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Abstract

Abstract: The distribution of apples usually features occlusion and small and dense targets. To address these issues, a target detection algorithm was proposed based on an improved YOLOv5 model. Specifically, this paper added the coordinate attention (CA) mechanism, receptive field block (RFB), and adaptively spatial feature fusion (ASFF) modules to the YOLOv5, enhancing the ability to detect small targets. Additionally, the proposed algorithm replaced the CIoU in YOLOv5 with SIoU to improve the target detection box's prediction accuracy. Finally, some normal convolutions were replaced with depthwise separable convolutions (DSC), effectively reducing the calculation burden. Experiment results show that the comprehensive performance of the improved YOLOv5 is better than the original YOLOv5 and other algorithms. Moreover, its mAP value is improved by 9.6% in comparison with the original YOLOv5.

Recommended Citation

Liu, Zilong and Zhang, Lei (2025) "Detection of Small Apple Targets Based on Improved YOLOv5 in Natural Environments," Journal of System Simulation: Vol. 37: Iss. 8, Article 18.
DOI: 10.16182/j.issn1004731x.joss.24-0260
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss8/18

First Page

2124

Last Page

2138

CLC

TP391

Recommended Citation

Liu Zilong, Zhang Lei. Detection of Small Apple Targets Based on Improved YOLOv5 in Natural Environments[J]. Journal of System Simulation, 2025, 37(8): 2124-2138.

Corresponding Author

Zhang Lei

DOI

10.16182/j.issn1004731x.joss.24-0260