Improved weed identification algorithm based on YOLOv5-SPD

YANG Zhifan; LI Haibo

doi:10.12299/jsues.23-0037

Volume 38 Issue 1

Feb. 2024

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Article Navigation > Journal of Shanghai University of Engineering Science > 2024 > 38(1): 75-82

YANG Zhifan, LI Haibo. Improved weed identification algorithm based on YOLOv5-SPD[J]. Journal of Shanghai University of Engineering Science, 2024, 38(1): 75-82. doi: 10.12299/jsues.23-0037

Citation:

YANG Zhifan, LI Haibo. Improved weed identification algorithm based on YOLOv5-SPD[J]. Journal of Shanghai University of Engineering Science, 2024, 38(1): 75-82. doi: 10.12299/jsues.23-0037

YANG Zhifan, LI Haibo. Improved weed identification algorithm based on YOLOv5-SPD[J]. Journal of Shanghai University of Engineering Science, 2024, 38(1): 75-82. doi: 10.12299/jsues.23-0037

Citation:

YANG Zhifan, LI Haibo. Improved weed identification algorithm based on YOLOv5-SPD[J]. Journal of Shanghai University of Engineering Science, 2024, 38(1): 75-82. doi: 10.12299/jsues.23-0037

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Improved weed identification algorithm based on YOLOv5-SPD

doi: 10.12299/jsues.23-0037

YANG Zhifan,
LI Haibo^,

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2023-02-27
Publish Date: 2024-03-30

Abstract

Abstract

Accurate identification of weeds is the primary prerequisite for achieving machine replacement of manual weeding. The target of nascent weeds is small, making identification difficult. YOLOv5-SPD has good performance in small target recognition, but its robustness and accuracy still need to be improved. Adding channel attention mechanism on the basis of YOLOv5-SPD can strengthen the weight value of effective features, making the learning of the network more targeted. At the same time, replacing the generalized intersection over union (GIoU) loss function with complete intersection over union (CIoU) can effectively solve the problem of border coincidence, the height width ratio of the target box and the prediction box, and the relationship between the center point, there by making the weed prediction box closer to the real box. The experimental results on the weed dataset show that the improved network detection accuracy reaches 70.3% with an accuracy rate of 94.1% which is 4.7% and 2.8% higher than the original YOLOv5-SPD.
- attention mechanism,
- boundary loss function,
- YOLOv5-SPD algorithm,
- weed identification

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References(16)

References

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