Open AccessLightweight detection method for real-time monitoring tomato growth based on improved YOLOv5s
Author(s) -
Suyu Tian,
Chao Fang,
Xiaogang Zheng,
Jue Liu
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3368914
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In order to monitor the growth and development of tomatoes, and improve the efficiency of flower and fruit thinning and tomato picking, we propose a model TF-YOLOv5s for the detection of tomato flowers and fruits in natural environments. Based on the YOLOv5s model, a C3Faster module is introduced to reduce the number of parameters and calculations while maintaining detection accuracy. The regular convolution is replaced by depth-wise separable convolution (DWConv) to avoid parameter redundancy. To improve the convergence and accuracy of the model, we replace Complete Intersection over Union (CIoU) loss with Efficient Intersection over Union (EIOU) loss. The Squeeze and Excitation (SE) attention mechanism is added to improve the model’s sensitivity to the features of the tomato flowers and fruits. Compared with the baseline model, the number of parameters is reduced by 54.5%, the weight file is reduced by 52.8%, the Floating-point Operation Per second (FLOPs) is reduced by 48.7%, and the detection accuracy of tomato flowers and fruits is increased by 1.4%. Furthermore, the improved algorithm is deployed on two edge computing devices to verify its effectiveness. Experimental results show that the algorithm in this paper can achieve high detection with less computational resources. This algorithm has the potential value of application in practical tomato production.