MAFIKD: A Real-Time Pest Detection Method Based on Knowledge Distillation

The significant damage caused by pests to crops has always been a pressing issue in agricultural production. To address the problems of low recognition accuracy, weak feature extraction capability, and poor robustness of lightweight pest detection models, this study proposes a knowledge distillation...

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Veröffentlicht in:	IEEE sensors journal 2024-10, Vol.24 (20), p.33715-33735
Hauptverfasser:	Xu, Delong, Dong, Yanqi, Ma, Zhibin, Zi, Jiali, Xu, Nuo, Xia, Yi, Li, Zijie, Xu, Fu, Chen, Feixiang
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models Adaptive algorithms Adaptive sampling Agricultural production Applications programs Artificial neural networks Computational complexity Computational modeling Correlation Crop production Damage detection Feature extraction Fine-grained feature Frames per second knowledge distillation Knowledge engineering Knowledge management Knowledge transfer Mobile computing multiattention fusion Neural networks Object recognition pest detection Pests Real time
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