Tesi etd-02022023-134057 |
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Tipo di tesi
Tesi di laurea magistrale
URN
etd-02022023-134057
Titolo
Comparative study of YOLO-based object detection for automated damage assessment in vehicles
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Relatori
.
relatore Prof. Cimino, Mario Giovanni Cosimo Antonio
relatore Prof. Vaglini, Gigliola
tutor Ing. Xhani, Orges
relatore Prof. Vaglini, Gigliola
tutor Ing. Xhani, Orges
Parole chiave
- Artificial Intelligence
- Damage Detection
- Deep Learning
- Vehicle damage assessment
- YOLO
Data inizio appello
17/02/2023
Consultabilità
Non consultabile
Data di rilascio
17/02/2093
Riassunto (Inglese)
Riassunto (Italiano)
Vehicle damage assessment is an important task in different industries, as it helps determine the repair cost and maintenance requirements; it also assists in preventing fraud during the acquisition and management of second-hand vehicles. In this context automated damage assessment can save time and reduce subjectivity in manual inspection. YOLO(You Only Look Once) is currently one of the state-of-art object detection model in the field of Computer Vision, showing remarkable results in both speed and accuracy. In this study, we specifically compared the performance of two YOLO versions: YoloV5 and YoloV7. These two models were evaluated in terms of accuracy and speed, and results were analyzed to determine the best model for distinct business cases.
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