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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) 
Raimundo, A., Pavia, J. P., Sebastião, P. & Postolache, O. (2023). YOLOX-Ray: An efficient attention-based single-staged object detector tailored for industrial inspections. Sensors. 23 (10)
Exportar Referência (IEEE) 
A. S. Raimundo et al.,  "YOLOX-Ray: An efficient attention-based single-staged object detector tailored for industrial inspections", in Sensors, vol. 23, no. 10, 2023
Exportar BibTeX 
@article{raimundo2023_1732212722337,
	author = "Raimundo, A. and Pavia, J. P. and Sebastião, P. and Postolache, O.",
	title = "YOLOX-Ray: An efficient attention-based single-staged object detector tailored for industrial inspections",
	journal = "Sensors",
	year = "2023",
	volume = "23",
	number = "10",
	doi = "10.3390/s23104681",
	url = "https://www.mdpi.com/1424-8220/23/10/4681"
}
Exportar RIS 
TY  - JOUR
TI  - YOLOX-Ray: An efficient attention-based single-staged object detector tailored for industrial inspections
T2  - Sensors
VL  - 23
IS  - 10
AU  - Raimundo, A.
AU  - Pavia, J. P.
AU  - Sebastião, P.
AU  - Postolache, O.
PY  - 2023
SN  - 1424-8220
DO  - 10.3390/s23104681
UR  - https://www.mdpi.com/1424-8220/23/10/4681
AB  - Industrial inspection is crucial for maintaining quality and safety in industrial processes. Deep learning models have recently demonstrated promising results in such tasks. This paper proposes YOLOX-Ray, an efficient new deep learning architecture tailored for industrial inspection. YOLOX-Ray is based on the You Only Look Once (YOLO) object detection algorithms and integrates the SimAM attention mechanism for improved feature extraction in the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN). Moreover, it also employs the Alpha-IoU cost function for enhanced small-scale object detection. YOLOX-Ray’s performance was assessed in three case studies: hotspot detection, infrastructure crack detection and corrosion detection. The architecture outperforms all other configurations, achieving mAP50  values of 89%, 99.6% and 87.7%, respectively. For the most challenging metric, mAP50:95, the achieved values were 44.7%, 66.1% and 51.8%, respectively. A comparative analysis demonstrated the importance of combining the SimAM attention mechanism with Alpha-IoU loss function for optimal performance. In conclusion, YOLOX-Ray’s ability to detect and to locate multi-scale objects in industrial environments presents new opportunities for effective, efficient and sustainable inspection processes across various industries, revolutionizing the field of industrial inspections.
ER  -