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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Zhang, Y., Postolache, O. & Mi, C. (2024). Techniques for target detection and localization at bulk cargo terminals combining morphological algorithms and improved YOLOv5. In 2024 International Symposium on Sensing and Instrumentation in 5G and IoT Era (ISSI). Lagoa, Portugal: IEEE.
Exportar Referência (IEEE) 
Y. Zhang et al.,  "Techniques for target detection and localization at bulk cargo terminals combining morphological algorithms and improved YOLOv5", in 2024 Int. Symp. on Sensing and Instrumentation in 5G and IoT Era (ISSI), Lagoa, Portugal, IEEE, 2024
Exportar BibTeX 
@inproceedings{zhang2024_1782666008521,
	author = "Zhang, Y. and Postolache, O. and Mi, C.",
	title = "Techniques for target detection and localization at bulk cargo terminals combining morphological algorithms and improved YOLOv5",
	booktitle = "2024 International Symposium on Sensing and Instrumentation in 5G and IoT Era (ISSI)",
	year = "2024",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/ISSI63632.2024.10720493",
	publisher = "IEEE",
	address = "Lagoa, Portugal",
	organization = "",
	url = "https://ieeexplore.ieee.org/document/10720493"
}
Exportar RIS 
TY  - CPAPER
TI  - Techniques for target detection and localization at bulk cargo terminals combining morphological algorithms and improved YOLOv5
T2  - 2024 International Symposium on Sensing and Instrumentation in 5G and IoT Era (ISSI)
AU  - Zhang, Y.
AU  - Postolache, O.
AU  - Mi, C.
PY  - 2024
DO  - 10.1109/ISSI63632.2024.10720493
CY  - Lagoa, Portugal
UR  - https://ieeexplore.ieee.org/document/10720493
AB  - As automation technologies become increasingly prevalent at bulk cargo terminals, efficient and accurate target detection and localization technologies are crucial. This paper proposes an enhanced YOLOv5 detection method, incorporating a CA mechanism to effectively enhance the model's extraction of spatial information, thereby improving the accuracy of target localization. Moreover, the IoU loss function is modified to SIoU, optimizing the impact of bounding box scaling and rotation on predictive performance. To further enhance the localization accuracy of bulk unloading hoppers, this study introduces a secondary localization method based on traditional morphological algorithms, capable of precisely detecting the position of the hopper. Experimental results demonstrate that the proposed methods achieve high accuracy and reliability in detecting vehicles and bulk unloading hoppers at bulk cargo terminals. 
ER  -