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Volume 107
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Proceeding Paper

Explainable Artificial Intelligence for Object Detection in the Automotive Sector †

by
Marios Siganos
1,
Panagiotis Radoglou-Grammatikis
1,2,*,
Thomas Lagkas
3,
Vasileios Argyriou
4,
Sotirios Goudos
5,
Konstantinos E. Psannis
6,
Konstantinos-Filippos Kollias
2,
George F. Fragulis
2 and
Panagiotis Sarigiannidis
2
1
K3Y Ltd., Studentski District, Vitosha Quarter, Bl. 9, 1700 Sofia, Bulgaria
2
Department of Electrical and Computer Engineering, University of Western Macedonia, Campus ZEP Kozani, 50100 Kozani, Greece
3
Department of Informatics, Democritus University of Thrace, Kavala Campus, 65404 Kavala, Greece
4
Department of Networks and Digital Media, Kingston University London, Penrhyn Road, Kingston upon Thames, Surrey KT1 2EE, UK
5
School of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
6
Department of Applied Informatics, School of Information Sciences, University of Macedonia, 156 Egnatia Street, 54636 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
†
Presented at the 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society, Aizuwakamatsu City, Japan, 20–26 January 2025.
Eng. Proc. 2025, 107(1), 44; https://doi.org/10.3390/engproc2025107044
Published: 1 September 2025
(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)
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Abstract

In the automotive domain, object detection is pivotal for enhancing safety and autonomy through the identification of various objects of interest. However, insights into the influential image pixels in the detection process are often lacking. Recognizing these significant regions within the image not only enriches our qualitative understanding of the model’s functionality but also empowers us to refine and optimize its performance. Employing Explainable Artificial Intelligence (XAI), we present an XAI component in this paper. This component explains the predictions made by a pre-trained object detection model for a given image by generating heatmaps that highlight the most critical regions in the image for the detected objects.
Keywords: artificial intelligence; automotive; EigenCAM; explainable ai; explainable artificial intelligence; object detection; visual XAI; XAI; YOLO artificial intelligence; automotive; EigenCAM; explainable ai; explainable artificial intelligence; object detection; visual XAI; XAI; YOLO

Share and Cite

MDPI and ACS Style

Siganos, M.; Radoglou-Grammatikis, P.; Lagkas, T.; Argyriou, V.; Goudos, S.; Psannis, K.E.; Kollias, K.-F.; Fragulis, G.F.; Sarigiannidis, P. Explainable Artificial Intelligence for Object Detection in the Automotive Sector. Eng. Proc. 2025, 107, 44. https://doi.org/10.3390/engproc2025107044

AMA Style

Siganos M, Radoglou-Grammatikis P, Lagkas T, Argyriou V, Goudos S, Psannis KE, Kollias K-F, Fragulis GF, Sarigiannidis P. Explainable Artificial Intelligence for Object Detection in the Automotive Sector. Engineering Proceedings. 2025; 107(1):44. https://doi.org/10.3390/engproc2025107044

Chicago/Turabian Style

Siganos, Marios, Panagiotis Radoglou-Grammatikis, Thomas Lagkas, Vasileios Argyriou, Sotirios Goudos, Konstantinos E. Psannis, Konstantinos-Filippos Kollias, George F. Fragulis, and Panagiotis Sarigiannidis. 2025. "Explainable Artificial Intelligence for Object Detection in the Automotive Sector" Engineering Proceedings 107, no. 1: 44. https://doi.org/10.3390/engproc2025107044

APA Style

Siganos, M., Radoglou-Grammatikis, P., Lagkas, T., Argyriou, V., Goudos, S., Psannis, K. E., Kollias, K.-F., Fragulis, G. F., & Sarigiannidis, P. (2025). Explainable Artificial Intelligence for Object Detection in the Automotive Sector. Engineering Proceedings, 107(1), 44. https://doi.org/10.3390/engproc2025107044

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