Deep Neural Network Based Performance Evaluation and Comparative Analysis of Human Detection in Crowded Images Using YOLO Models
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9518-9_37
Reference16 articles.
1. Jiang P et al (2022) A review of YOLO algorithm developments. Procedia Comput Sci 199:1066–1073
2. Nepal U, Eslamiat H (2022) Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs. Sensors 22(2):464
3. Sozzi M et al (2022) Automatic bunch detection in white grape varieties using YOLOv3, YOLOv4, and YOLOv5 deep learning algorithms. Agronomy 12(2):319
4. Liu K et al (2021) Performance validation of YOLO variants for object detection. In: Proceedings of the 2021 international conference on bioinformatics and intelligent computing
5. Li S et al (2021) YOLO-firi: improved YOLOv5 for infrared image object detection. IEEE access 9:141861–141875.
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