<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The successful application of cutting-edge computer vision technology to automatic insect classification has long been a focus of research in insect taxonomy. The results of this research have a wide range of applications in areas such as environmental monitoring, pest diagnosis and epidemiology. However, there is still a gap between the current techniques used in automatic insect classification and the latest computer vision techniques. The research in this paper is conducted on Lepidoptera, a class of insects that are widely infested, including butterflies and moths. The study focuses on the application of deep learning algorithms in image processing of Lepidoptera insects. In order to improve the recognition rate for Lepidoptera insect recognition, this paper uses a detection model based on deep neural networks to realize the recognition of Lepidoptera insects in complex environments. Specifically, the yolov7 algorithm is adopted as the basic model for this experiment, and the reasons for using this model are explained in terms of the splicing of network modules, loss function, positive sample allocation strategy, and the merging of convolution and normalization, respectively. Through experiments, it is proved that the algorithm can effectively improve the gesture recognition rate, the recognition accuracy reaches 79.5%, and the recognition speed is as high as 33.08it/s.</p>
</abstract>

The successful application of cutting-edge computer vision technology to automatic insect classification has long been a focus of research in insect taxonomy. The results of this research have a wide range of applications in areas such as environmental monitoring, pest diagnosis and epidemiology. However, there is still a gap between the current techniques used in automatic insect classification and the latest computer vision techniques. The research in this paper is conducted on Lepidoptera, a class of insects that are widely infested, including butterflies and moths. The study focuses on the application of deep learning algorithms in image processing of Lepidoptera insects. In order to improve the recognition rate for Lepidoptera insect recognition, this paper uses a detection model based on deep neural networks to realize the recognition of Lepidoptera insects in complex environments. Specifically, the yolov7 algorithm is adopted as the basic model for this experiment, and the reasons for using this model are explained in terms of the splicing of network modules, loss function, positive sample allocation strategy, and the merging of convolution and normalization, respectively. Through experiments, it is proved that the algorithm can effectively improve the gesture recognition rate, the recognition accuracy reaches 79.5%, and the recognition speed is as high as 33.08it/s.

Deep Learning Based Recognition of Lepidoptera Insects

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Deep Learning Based Recognition of Lepidoptera Insects"}

The successful application of cutting-edge computer vision technology to automatic insect classification has long been a focus of research in insect taxonomy....

Arithmetics	mAP/%	Speed
YOLOv5m6	78.6%	22.36it/s
YOLOv5s6	75.8%	24.69it/s
YOLOv7	79.5%	33.08it/s

Deep Learning Based Recognition of Lepidoptera Insects

Online veröffentlicht: 16. März 2024

Seitenbereich: 20 - 28

DOI: https://doi.org/10.2478/ijanmc-2023-0073

Schlüsselwörter
Deep Learning, Deep Neural Networks, Yolov7, Recognition of Lepidopteran Insects

© 2023 Chao He et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

YOLOv5m6, YOLOv5s6, YOLOv7 Performance Comparison

Deep Learning Based Recognition of Lepidoptera Insects

Online veröffentlicht: 16. März 2024

Seitenbereich: 20 - 28

DOI: https://doi.org/10.2478/ijanmc-2023-0073

SchlüsselwörterDeep Learning, Deep Neural Networks, Yolov7, Recognition of Lepidopteran Insects

© 2023 Chao He et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

YOLOv5m6, YOLOv5s6, YOLOv7 Performance Comparison

Schlüsselwörter
Deep Learning, Deep Neural Networks, Yolov7, Recognition of Lepidopteran Insects