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Author(s): Raimundo, A.
Pavia, J. P.
Sebastião, P.
Postolache, O.
Date: 2023
Title: YOLOX-Ray: An efficient attention-based single-staged object detector tailored for industrial inspections
Journal title: Sensors
Volume: 23
Number: 10
Reference: 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), 4681.
ISSN: 1424-8220
DOI (Digital Object Identifier): 10.3390/s23104681
Keywords: Industrial inspections
Computer vision
Deep learning
Object detection
Attention mechanisms
Loss function
Abstract: 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.
Peerreviewed: yes
Access type: Open Access
Appears in Collections:IT-RI - Artigos em revistas científicas internacionais com arbitragem científica

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