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  4. Vol 7, Issue 3, 2023

Research on an Improved YOLOV8 Image Segmentation Model for Crop Pests

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DOI: 10.23977/acss.2023.070301 | Downloads: 484 | Views: 3083

Author(s)

Jichang Kang 1, Lianjun Zhao 1, Kangtao Wang 1, Kunpeng Zhang 1

Affiliation(s)

1 Shandong University of Technology, Zibo, Shangdong, 255000 China

Corresponding Author

Jichang Kang

ABSTRACT

With the change of ecosystem, there are more and more kinds of crop diseases and insect pests, and the harm is becoming more and more serious. Preventing crop diseases and insect pests is the premise to ensure crop yield. Image segmentation technology is to divide a number of specific targets and regions with different characteristics in the image according to the requirements through pixel-level classification scheme, which is the first important link of image analysis. In this article, Simulated Annealing (SA) algorithm is used to optimize YOLOV8. The main purpose is to randomly find the optimal solution of the loss function in the last layer of convolutional neural network (CNN) with SA algorithm, and then update the weights and offsets of the previous layer with this solution. The CNN structure also uses the dropout regularization method to effectively reduce the influence of over-fitting. The simulation results show that compared with YOLOV7 algorithm, the average accuracy of disease identification of improved YOLOV8 is obviously higher. The pest identification model based on the improved YOLOV8 algorithm has more advantages than YOLOV7 algorithm in both accuracy and efficiency. The proposed method achieves the best detection performance on large-scale public data sets, and also performs well in the task of crop pest detection studied in this article.

KEYWORDS

YOLOV8, Simulated annealing algorithm, Crop pests, Image segmentation

CITE THIS PAPER

Jichang Kang, Lianjun Zhao, Kangtao Wang, Kunpeng Zhang. Research on an Improved YOLOV8 Image Segmentation Model for Crop Pests. Advances in Computer, Signals and Systems (2023) Vol. 7: 1-8. DOI: http://dx.doi.org/10.23977/acss.2023.070301.

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