Ponencia
Robustness Testing of a Machine Learning-based Road Object Detection System: An Industrial Case
Autor/es | Wozniak, Anne-Laure
Segura Rueda, Sergio Mazo, Raúl Leroy, Sarah |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2022 |
Fecha de depósito | 2022-11-07 |
Publicado en |
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ISBN/ISSN | 978-145039319-5 |
Resumen | artifi-cial intelligence (AI), methods have been proposed and evaluated in academia to assess the reliability of these systems. In the context of computer vision, some approaches use the generation of images altered by ... artifi-cial intelligence (AI), methods have been proposed and evaluated in academia to assess the reliability of these systems. In the context of computer vision, some approaches use the generation of images altered by common perturbations and realistic transformations to assess the robustness of systems. To better understand the strengths and limitations of these approaches, we report the results obtained on an industrial case of a road object detection system. By compar-ing these results with those of reference models, we identify areas for improvement regarding the robustness of the system and the metrics used for this evaluation. CCS CONCEP |
Agencias financiadoras | French National Agency of Research and Technology (ANRT) Junta de Andalucía Ministerio de Ciencia e Innovación (MICIN). España |
Identificador del proyecto | CIFRE N°2020/0754
P18-FR-2895 (EKIPMENT-PLUS) US-1264651 (APOLO) RTI2018-101204-B-C21 (HORATIO) |
Cita | Wozniak, A., Segura Rueda, S., Mazo, R. y Leroy, S. (2022). Robustness Testing of a Machine Learning-based Road Object Detection System: An Industrial Case. En SE4RAI 2022: 1st IEEE/ACM International Workshop on Software Engineering for Responsible Artificial Intelligence Pittsburgh, PA, USA: IEEE Computer Society. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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wozniak22-SR4RAI.pdf | 1.476Mb | [PDF] | Ver/ | |