Leveraging robust CNN architectures for real-time object recognition from conveyor belt

Leveraging robust CNN architectures for real-time object recognition from conveyor belt

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Moon, Nowrin Tasnim, Siddiqua, Sabiha Afrin, Parvin, Shahana, Muntaha, Sidratul, Hassan, K.M. Mehedi
Άλλοι συγγραφείς: Mostakim, Moin
Μορφή: Thesis
Γλώσσα:English
Έκδοση: Brac University 2023
Θέματα:
Neural network
Object detection
Conveyor belt
VGG16
ResNet
MobileNet
YOLOv5
YOLOv7
Convolutional Neural Network
CNN
Vision transformer
Neural networks (Computer science)
Διαθέσιμο Online:http://hdl.handle.net/10361/19296

Διαδίκτυο

http://hdl.handle.net/10361/19296

Παρόμοια τεκμήρια