FESNet: Frequency-Enhanced Saliency Detection Network for Grain Pest Segmentation

As insect infestation is the leading factor accounting for nutritive and economic losses in stored grains, it is important to detect the presence and number of insects for the sake of taking proper control measures. Inspired by the human visual attention mechanism, we propose a U-net-like frequency-...

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Veröffentlicht in:	Insects (Basel, Switzerland) Jg. 14; H. 2; S. 99
Hauptverfasser:	Yu, Junwei, Zhai, Fupin, Liu, Nan, Shen, Yi, Pan, Quan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 17.01.2023 MDPI
Schlagworte:	Ablation Accuracy Acoustics Algorithms Analysis Annotations Artificial neural networks Classification Clutter data collection Datasets deep frequency feature Deep learning Detectors Discrete cosine transform Discrete Wavelet Transform Diseases and pests Economic impact Economics Fourier transforms Grain grain pest segmentation Grain storage Growth humans image analysis Image classification Image segmentation Infestation Insect control insect infestations Insect pests Insects Machine learning Methods Neural networks Object recognition Pests Pixels Population density Receptive field Salience Signal processing Sound detecting and ranging Spatial data Visual perception visual saliency wavelet Wavelet transforms China discrete cosine transform discrete wavelet transform deep frequency feature grain pest segmentation visual saliency
ISSN:	2075-4450, 2075-4450
Online-Zugang:	Volltext
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