Improved Method for Apple Fruit Target Detection Based on YOLOv5s

Huaiwen Wang, Jianguo Feng and Honghuan Yin ()
Additional contact information
Huaiwen Wang: Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, No. 409 Guangrong Road, Beichen District, Tianjin 300134, China
Jianguo Feng: Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, No. 409 Guangrong Road, Beichen District, Tianjin 300134, China
Honghuan Yin: Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, No. 409 Guangrong Road, Beichen District, Tianjin 300134, China

Agriculture, 2023, vol. 13, issue 11, 1-16

Abstract: Images captured using unmanned aerial vehicles (UAVs) often exhibit dense target distribution and indistinct features, which leads to the issues of missed detection and false detection in target detection tasks. To address these problems, an improved method for small target detection called YOLOv5s is proposed to enhance the detection accuracy for small targets such as apple fruits. By applying improvements to the RFA module, DFP module, and Soft-NMS algorithm, as well as integrating these three modules together, accurate detection of small targets in images can be achieved. Experimental results demonstrate that the integrated, improved model achieved a significant improvement in detection accuracy, with precision, recall, and mAP increasing by 3.6%, 6.8%, and 6.1%, respectively. Furthermore, the improved method shows a faster convergence speed and lower loss value during the training process, resulting in higher recognition accuracy. The results of this study indicate that the proposed improved method exhibits a good performance in apple fruit detection tasks involving UAV imagery, which is of great significance for fruit yield estimation. The research findings demonstrate the effectiveness and feasibility of the improved method in addressing small target detection tasks, such as apple fruit detection.

Keywords: apple; target detection; YOLOv5s; Dual-Feature Pool (DFP) structure (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/11/2167/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/11/2167/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:11:p:2167-:d:1283002

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().