A Transfer Learning Approach for Identification of Two-Wheeler Brand and Model
Título del documento: A Transfer Learning Approach for Identification of Two-Wheeler Brand and Model
Revista: Computación y sistemas
Base de datos:
Número de sistema: 000607919
ISSN: 1405-5546
Autores: 1
1
1
1
Instituciones: 1Thiagarajar College of Engineering, Electronics and Communication Engineering, India
Año:
Periodo: Abr-Jun
Volumen: 28
Número: 2
Paginación: 543-551
País: México
Idioma: Inglés
Resumen en inglés Motorcycles were originally designed to provide safer, more efficient, and more comfortable rides, but they are now also used for criminal purposes. Due to overspeeding, motorcycles often get involved in accidents, and it is challenging for law enforcement officials to identify the culprits from CCTV footage or spectator accounts. This paper presents a solution to this challenge by using a pre-trained deep learning model to detect, classify, and identify motorcycle models. To overcome the limited availability of annotated bike databases, this proposed work created a new bike dataset that includes 5000 annotated images sourced from major search engines, CCTV footage, and manual captures from bike showrooms. Then the bike brand was identified in 27 classes by the Faster RCNN pre-trained model and achieved an accuracy of 94.35%. The proposed model was compared with the other pre-trained models such as YOLO V5 and MobileDET, among these, the Faster RCNN provided better identification accuracy.
Keyword: Bike dataset,
Wheel rim,
Headlamp,
Yolo V5,
MobileDET,
Faster RCNN,
Imbalanced learning,
And weighted loss learning
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