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Title: COMPARISON OF THE PERFORMANCE OF DIFFERENT NEURAL NETWORK ARCHITECTURES AND PRE-TRAINED NEURAL NETWORKS FOR THE CLASSIFICATION OF FOREST FLORA AND FAUNA
COMPARISON OF THE PERFORMANCE OF DIFFERENT NEURAL NETWORK ARCHITECTURES AND PRE-TRAINED NEURAL NETWORKS FOR THE CLASSIFICATION OF FOREST FLORA AND FAUNA

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Witold Prusak; Aleksander Skrzypiec; Tymoteusz Turlej
10.5593/sgem2023/2.1/s07.05
10.5593/sgem2023/2.1
1314-2704
978-619-7603-57-6
English
23
2.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Informatics
The aim of this project is to compare the effectiveness of different neural network architectures and pre-trained models for the classification of forest flora and fauna. Experiments were conducted on a dataset containing images of selected plants and animals found in the forest. The project covers the basics of convolutional neural networks, and compares the architectures of the networks used in the study. The results of the experiments compare the effectiveness of different convolutional neural network architectures and pre-trained models, such as EfficientNet or ResNet50. The study includes the training times of selected neural networks, classification times of individual images, and their effectiveness. Additionally, the impact of pre-training networks on the ImageNet dataset on the quality of neural network classification was compared. The purpose of this project was to determine the neural network with the most optimal parameters for use in our robot, “Rumcajs”, which is to be used for monitoring and mapping forest flora and fauna by classifying the segmented images with Segment Anything Model from our advanced camera systems.
[1] Hiary, H., Saadeh, H., Saadeh, M. and Yaqub, M. (2018), Flower classification using deep convolutional neural networks. IET Comput. Vis., 12: 855-862.
[2] Lu, J.; Tan, L.; Jiang, H. Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification. Agriculture 2021, 11, 707.
[3] Zhao, Yujin, et al. "Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China." Remote Sensing of Environment 213 (2018): 104-114.
[4] Rovero, Francesco, et al. "Estimating species richness and modelling habitat preferences of tropical forest mammals from camera trap data." PloS one 9.7 (2014): e103300.
[5] Krizhevsky.A, Sutskever.I, Hinton.G.E, ImageNet Classification with Deep Convolutional Neural Networks, Proceedings of the 25th International Conference on Neural Information Processing Systems (Lake Tahoe, NV, Dec. 2012), 1097–1105
[6] Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
[7] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[8] Tan, Mingxing, and Quoc Le. "Efficientnet: Rethinking model scaling for convolutional neural networks." International conference on machine learning. PMLR, 2019.
[9] Tan, Mingxing, and Quoc Le. "Efficientnetv2: Smaller models and faster training." International conference on machine learning. PMLR, 2021.
[10] Wang, Weihong, Jian Zhang, and Chunhua Shen. "Improved human detection and classification in thermal images." 2010 IEEE International Conference on Image Processing. IEEE, 2010.
[11] Cordero, R. J., Ellie Rose Mattoon, and Arturo Casadevall. "Fungi are colder than their surroundings." BioRxiv (2020).
The work was created as a result of the research project No. 67/GRANT/2023 and 5873 financed by the Rector of AGH, IDUB and the Faculty of Mechanical Engineering and Robotics AGH
conference
https://doi.org/10.5593/sgem2023/2.1/s07.05
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Proceedings of 23rd International Multidisciplinary Scientific GeoConference SGEM 2023
23rd International Multidisciplinary Scientific GeoConference SGEM 2023, 03 - 09 July, 2023
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.
35-40
03 - 09 July, 2023
website
9085
Nature, Segment Anything Model, Neural Network, Classification, Data Science