Detection Method of Forest Pests Based on Attention Mechanism and Lightweight YOLOv5

Cha Kehao, Song Xudong
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:253-264, 2024.

Abstract

In view of the forestry pest identification research is less, manual identification time-consuming and labor-intensive low accuracy. An attention-based and lightweight YOLOv5 forestry pest identification method was proposed. First of all, the traditional backbone network CSPDarknet is modified to the improved ShuffleNetV2, which simplifies the network structure and makes the network more lightweight; Secondly, the hybrid attention mechanism CBAM (Convolutional Block Attention Module) is introduced to increase the perception ability of cyberspace and channel features while keeping the parameters and calculation load basically unchanged. Finally, the loss function is replaced by WIoU to improve model training to speed up model convergence and improve regression accuracy. The average detection accuracy of the improved model is 89.9%, which is 3.6% higher than that of the original YOLOv5s algorithm; The parameters decreased by 3213050 and the calculation amount decreased by 7.8. The improved model improves the detection accuracy and reduces the parameters and calculation amount. Compared with other advanced algorithms, the algorithm in this paper has excellent performance, which can provide reference for forest pest identification and management.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-kehao24a, title = {Detection Method of Forest Pests Based on Attention Mechanism and Lightweight YOLOv5}, author = {Kehao, Cha and Xudong, Song}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {253--264}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/kehao24a/kehao24a.pdf}, url = {https://proceedings.mlr.press/v245/kehao24a.html}, abstract = {In view of the forestry pest identification research is less, manual identification time-consuming and labor-intensive low accuracy. An attention-based and lightweight YOLOv5 forestry pest identification method was proposed. First of all, the traditional backbone network CSPDarknet is modified to the improved ShuffleNetV2, which simplifies the network structure and makes the network more lightweight; Secondly, the hybrid attention mechanism CBAM (Convolutional Block Attention Module) is introduced to increase the perception ability of cyberspace and channel features while keeping the parameters and calculation load basically unchanged. Finally, the loss function is replaced by WIoU to improve model training to speed up model convergence and improve regression accuracy. The average detection accuracy of the improved model is 89.9%, which is 3.6% higher than that of the original YOLOv5s algorithm; The parameters decreased by 3213050 and the calculation amount decreased by 7.8. The improved model improves the detection accuracy and reduces the parameters and calculation amount. Compared with other advanced algorithms, the algorithm in this paper has excellent performance, which can provide reference for forest pest identification and management.} }
Endnote
%0 Conference Paper %T Detection Method of Forest Pests Based on Attention Mechanism and Lightweight YOLOv5 %A Cha Kehao %A Song Xudong %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-kehao24a %I PMLR %P 253--264 %U https://proceedings.mlr.press/v245/kehao24a.html %V 245 %X In view of the forestry pest identification research is less, manual identification time-consuming and labor-intensive low accuracy. An attention-based and lightweight YOLOv5 forestry pest identification method was proposed. First of all, the traditional backbone network CSPDarknet is modified to the improved ShuffleNetV2, which simplifies the network structure and makes the network more lightweight; Secondly, the hybrid attention mechanism CBAM (Convolutional Block Attention Module) is introduced to increase the perception ability of cyberspace and channel features while keeping the parameters and calculation load basically unchanged. Finally, the loss function is replaced by WIoU to improve model training to speed up model convergence and improve regression accuracy. The average detection accuracy of the improved model is 89.9%, which is 3.6% higher than that of the original YOLOv5s algorithm; The parameters decreased by 3213050 and the calculation amount decreased by 7.8. The improved model improves the detection accuracy and reduces the parameters and calculation amount. Compared with other advanced algorithms, the algorithm in this paper has excellent performance, which can provide reference for forest pest identification and management.
APA
Kehao, C. & Xudong, S.. (2024). Detection Method of Forest Pests Based on Attention Mechanism and Lightweight YOLOv5. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:253-264 Available from https://proceedings.mlr.press/v245/kehao24a.html.

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