Comparison of CNN-Based Architectures for Detection of Different Object Classes

  • (1) Background: 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 civilians. A fast and accurate detection of 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 analyze big volumes of visual data in real-time. (2) Methods: The performance of the neural networks such as You Only Look Once (YOLO) v4-v8, Faster R-CNN, Single Shot MultiBox Detector (SSD), and EfficientDet has been analyzed using COCO2017, SARD, SeaDronesSee, and VisDrone2019 datasets. The main metrics for comparison were mAP, Precision, Recall, F1-Score, and the ability of the neural network to work in real-time. (3) Results: 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. (4) Conclusion: 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.

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Metadaten
URN:urn:nbn:de:kobv:526-opus4-19634
Publisher DOI:https://doi.org/10.3390/ai5040113
Author:Nataliya BilousORCiD, Vladyslav MalkoORCiD, Marcus FrohmeORCiDGND, Alina NechyporenkoORCiD
Parent Title (English):AI
Document Type:Article
Language:English
Year of Publication:2024
Volume:5
Issue:4
First Page:2300
Last Page:2320
Publisher:MDPI
Publishing Institution:Technische Hochschule Wildau
Tag:EfficientDet; SSD; YOLO; deep learning; neural network; object detection
Source:Bilous, N.; Malko, V.; Frohme, M.; Nechyporenko, A. Comparison of CNN-Based Architectures for Detection of Different Object Classes. AI 2024, 5, 2300-2320. https://doi.org/10.3390/ai5040113
Faculties an central facilities:Fachbereich Ingenieur- und Naturwissenschaften
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Funding:Publikationsfonds der TH Wildau
Licence (German): Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2024/11/19
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