@ARTICLE{Popek_Łukasz_Optimization_2023,
 author={Popek, Łukasz and Perz, Rafał and Galiński, Grzegorz and Abratański, Artur},
 volume={vol. 69},
 number={No 4},
 journal={International Journal of Electronics and Telecommunications},
 pages={825-831},
 howpublished={online},
 year={2023},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={The article presents research on animal detection in thermal images using the YOLOv5 architecture. The goal of the study was to obtain a model with high performance in detecting animals in this type of images, and to see how changes in hyperparameters affect learning curves and final results. This manifested itself in testing different values of learning rate, momentum and optimizer types in relation to the model’s learning performance. Two methods of tuning hyperparameters were used in the study: grid search and evolutionary algorithms. The model was trained and tested on an in-house dataset containing images with deer and wild boars. After the experiments, the trained architecture achieved the highest score for Mean Average Precision (mAP) of 83%. These results are promising and indicate that the YOLO model can be used for automatic animal detection in various applications, such as wildlife monitoring, environmental protection or security systems.},
 type={Article},
 title={Optimization of Animal Detection in Thermal Images Using YOLO Architecture},
 URL={http://journals.pan.pl/Content/129127/PDF-MASTER/25_4264_Popek_L_sk.pdf},
 doi={10.24425/ijet.2023.147707},
 keywords={artificial neural networks, YOLOv5, transfer learning, genetic algorithms, thermal imaging},
}