A General Image Super-Resolution Reconstruction Technique for Walnut Object Detection Model

Mingjie Wu, Xuanxi Yang, Lijun Yun (), Chenggui Yang, Zaiqing Chen and Yuelong Xia
Additional contact information
Mingjie Wu: School of Information, Yunnan Normal University, Kunming 650500, China
Xuanxi Yang: Centre for Planning and Policy Research, Yunnan Institute of Forest Inventory and Planning, Kunming 650500, China
Lijun Yun: School of Information, Yunnan Normal University, Kunming 650500, China
Chenggui Yang: School of Information, Yunnan Normal University, Kunming 650500, China
Zaiqing Chen: School of Information, Yunnan Normal University, Kunming 650500, China
Yuelong Xia: School of Information, Yunnan Normal University, Kunming 650500, China

Agriculture, 2024, vol. 14, issue 8, 1-24

Abstract: Object detection models are commonly used in yield estimation processes in intelligent walnut production. The accuracy of these models in capturing walnut features largely depends on the quality of the input images. Without changing the existing image acquisition devices, this study proposes a super-resolution reconstruction module for drone-acquired walnut images, named Walnut-SR, to enhance the detailed features of walnut fruits in images, thereby improving the detection accuracy of the object detection model. In Walnut-SR, a deep feature extraction backbone network called MDAARB (multilevel depth adaptive attention residual block) is designed to capture multiscale information through multilevel channel connections. Additionally, Walnut-SR incorporates an RRDB (residual-in-residual dense block) branch, enabling the module to focus on important feature information and reconstruct images with rich details. Finally, the CBAM (convolutional block attention module) attention mechanism is integrated into the shallow feature extraction residual branch to mitigate noise in shallow features. In 2× and 4× reconstruction experiments, objective evaluation results show that the PSNR and SSIM for 2× and 4× reconstruction reached 24.66 dB and 0.8031, and 19.26 dB and 0.4991, respectively. Subjective evaluation results indicate that Walnut-SR can reconstruct images with richer detail information and clearer texture features. Comparative experimental results of the integrated Walnut-SR module show significant improvements in mAP50 and mAP50:95 for object detection models compared to detection results using the original low-resolution images.

Keywords: intelligent walnut production; UAV image; walnut; super-resolution reconstruction; object detection (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/8/1279/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/8/1279/ (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:14:y:2024:i:8:p:1279-:d:1448908

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 ().