Version 1
: Received: 16 September 2024 / Approved: 17 September 2024 / Online: 17 September 2024 (11:08:53 CEST)
How to cite:
Bilous, N.; Malko, V.; Frohme, M.; Nechyporenko, A. Comparison of CNN-Based Architectures for Detection of Different Object Classe. Preprints2024, 2024091310. https://doi.org/10.20944/preprints202409.1310.v1
Bilous, N.; Malko, V.; Frohme, M.; Nechyporenko, A. Comparison of CNN-Based Architectures for Detection of Different Object Classe. Preprints 2024, 2024091310. https://doi.org/10.20944/preprints202409.1310.v1
Bilous, N.; Malko, V.; Frohme, M.; Nechyporenko, A. Comparison of CNN-Based Architectures for Detection of Different Object Classe. Preprints2024, 2024091310. https://doi.org/10.20944/preprints202409.1310.v1
APA Style
Bilous, N., Malko, V., Frohme, M., & Nechyporenko, A. (2024). Comparison of CNN-Based Architectures for Detection of Different Object Classe. Preprints. https://doi.org/10.20944/preprints202409.1310.v1
Chicago/Turabian Style
Bilous, N., Marcus Frohme and Alina Nechyporenko. 2024 "Comparison of CNN-Based Architectures for Detection of Different Object Classe" Preprints. https://doi.org/10.20944/preprints202409.1310.v1
Abstract
Detecting people and technical objects in various situations, such as natural disasters and warfare, is critical to search and rescue operations and the safety of the civilian. A fast and accurate detection for people and equipment can significantly increase the effectiveness of search and rescue missions and provide timely assistance to people. Computer vision and deep learning technologies play a key role in detecting the required objects due to their ability to analyse big volumes of visual data in real time. The performance of the neural networks such as YOLOv4-v8, Faster R-CNN, SSD, and EfficientDet has been analysed using COCO2017, SARD, SeaDronesSee, and VisDrone2019 datasets. Main criteria for comparison were mAP, Precision, Recall, F1-Score, and the ability of the neural network to work in real time. The most important metrics for evaluating the efficiency and performance of models for a given task are accuracy (mAP), F1-Score, and processing speed (FPS). These metrics allow us to evaluate both the accuracy of object recognition and the ability to use the models in real-world environments where high processing speed is important. Although different neural networks perform better on certain types of metrics, YOLO outperforms them on all metrics, showing the best results of mAP-0.88, F1 - 0.88, and FPS - 48, so the focus was on these models.
Keywords
deep learning; object detection; neural network; YOLO; SSD; EfficientDet
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
