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Deep Learning Based Recognition of Lepidoptera Insects

Chao He

Chao He

School of Computer Science and Engineering Xi’an Technological UniversityXi’an, China
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He, Chao
 e 
Pingping Liu

Pingping Liu

School of Computer Science and Engineering Xi’an Technological UniversityXi’an, China
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Liu, Pingping
  
16 mar 2024
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Pubblicato online: 16 mar 2024
Pagine: 20 - 28
DOI: https://doi.org/10.2478/ijanmc-2023-0073
Parole chiave
© 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.

Network structure of YOLOv7.
Network structure of YOLOv7.

Figure 2.

Network structure of the E-ELAN layer.
Network structure of the E-ELAN layer.

Figure 3.

Network structure of the MPConv layer.
Network structure of the MPConv layer.

Figure 4.

IOU loss calculation process.
IOU loss calculation process.

Figure 5.

Loss, Precision, Recall, mPA0.5 and mAP0.5-0.95 curves
Loss, Precision, Recall, mPA0.5 and mAP0.5-0.95 curves

Figure 6.

Lepidoptera Insect Detection Effect
Lepidoptera Insect Detection Effect

YOLOv5m6, YOLOv5s6, YOLOv7 Performance Comparison

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