A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention
Jiangxi University of Science and Technology
Abstract
A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model’s detection precision in complex environments by enhancing the…
Citation impact
- FWCI
- 114.39
- Percentile
- 100%
- References
- 29
Authors
5Topics & keywords
- Computer science
- Feature (linguistics)
- Convolution (computer science)
- Algorithm
- Pattern recognition (psychology)
- Artificial intelligence
- Precision and recall
- Artificial neural network
- Zero hunger