Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm

Frontiers in Plant Science

Plant Science

Hailong Zhu¹  Fenghua Wang²  Jin Jiang²  Zhexing Sun²  Yuan Tang²  Yu Chen²  Qinghui Lai² 

[1] Engineering Training Center, Kunming University of Science and Technology, Kunming, Yunnan, China;Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China;

关键词: improved YOLOv7;    Capsicum frutescens L.;    detection;    unstructured environment;    lightweight;   

DOI  :  10.3389/fpls.2023.1200144

 received in 2023-04-04, accepted in 2023-05-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionReal-time fruit detection is a prerequisite for using the Xiaomila pepper harvesting robot in the harvesting process.MethodsTo reduce the computational cost of the model and improve its accuracy in detecting dense distributions and occluded Xiaomila objects, this paper adopts YOLOv7-tiny as the transfer learning model for the field detection of Xiaomila, collects images of immature and mature Xiaomila fruits under different lighting conditions, and proposes an effective model called YOLOv7-PD. Firstly, the main feature extraction network is fused with deformable convolution by replacing the traditional convolution module in the YOLOv7-tiny main network and the ELAN module with deformable convolution, which reduces network parameters while improving the detection accuracy of multi-scale Xiaomila targets. Secondly, the SE (Squeeze-and-Excitation) attention mechanism is introduced into the reconstructed main feature extraction network to improve its ability to extract key features of Xiaomila in complex environments, realizing multi-scale Xiaomila fruit detection. The effectiveness of the proposed method is verified through ablation experiments under different lighting conditions and model comparison experiments.ResultsThe experimental results indicate that YOLOv7-PD achieves higher detection performance than other single-stage detection models. Through these improvements, YOLOv7-PD achieves a mAP (mean Average Precision) of 90.3%, which is 2.2%, 3.6%, and 5.5% higher than that of the original YOLOv7-tiny, YOLOv5s, and Mobilenetv3 models, respectively, the model size is reduced from 12.7 MB to 12.1 MB, and the model’s unit time computation is reduced from 13.1 GFlops to 10.3 GFlops.DiscussionThe results shows that compared to existing models, this model is more effective in detecting Xiaomila fruits in images, and the computational complexity of the model is smaller.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Jiang, Chen, Sun, Tang, Lai and Zhu