Back to Search Start Over

Dragon fruit detection in natural orchard environment by integrating lightweight network and attention mechanism

Authors :
Bin Zhang
Rongrong Wang
Huiming Zhang
Chenghai Yin
Yuyang Xia
Meng Fu
Wei Fu
Source :
Frontiers in Plant Science, Vol 13 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

An improved lightweight network (Improved YOLOv5s) was proposed based on YOLOv5s in this study to realise all-weather detection of dragon fruit in a complex orchard environment. A ghost module was introduced in the original YOLOv5s to realise the lightweight of the model. The coordinate attention mechanism was joined to make the model accurately locate and identify the dense dragon fruits. A bidirectional feature pyramid network was built to improve the detection effect of dragon fruit at different scales. SIoU loss function was adopted to improve the convergence speed during model training. The improved YOLOv5s model was used to detect a dragon fruit dataset collected in the natural environment. Results showed that the mean average precision (mAP), precision (P) and recall (R) of the model was 97.4%, 96.4% and 95.2%, respectively. The model size, parameters (Params) and floating-point operations (FLOPs) were 11.5 MB, 5.2 M and 11.4 G, respectively. Compared with the original YOLOv5s network, the model size, Params and FLOPs of the improved model was reduced by 20.6%, 18.75% and 27.8%, respectively. Meanwhile, the mAP of the improved model was improved by 1.1%. The results prove that the improved model had a more lightweight structure and better detection performance. Moreover, the average precision (AP) of the improved YOLOv5s for dragon fruit under the front light, back light, side light, cloudy day and night was 99.5%, 97.3%, 98.5%, 95.5% and 96.1%, respectively. The detection performance met the requirements of all-weather detection of dragon fruit and the improved model had good robustness. This study provides a theoretical basis and technical support for fruit monitoring based on unmanned aerial vehicle technology and intelligent picking based on picking robot technology.

Details

Language :
English
ISSN :
1664462X
Volume :
13
Database :
Directory of Open Access Journals
Journal :
Frontiers in Plant Science
Publication Type :
Academic Journal
Accession number :
edsdoj.33cdfb63e22147ceb443c9015c84cd5f
Document Type :
article
Full Text :
https://doi.org/10.3389/fpls.2022.1040923