magistrsko delo magistrskega študijskega programa II. stopnje Strojništvo
Matej Čampelj (Author), Rok Vrabič (Mentor), Miha Brojan (Co-mentor)

Abstract

Največji problem pri sortiranju smeti je prepoznavanje in klasifikacija smeti, ki je lahko direktna glede na fizikalne lastnosti ali pa indirektna s kamero. Pri slednji se po navadi uporabljajo nevronske mreže, naučene na pripravljeni učni množici za prepoznavanje objektov na sliki. V nalogi smo natrenirali algoritem YOLO na javno dostopni učni množici z imenom TACO. Za namen testiranja smo naredili testno robotsko celico, tako da smo izdelali mehko odrivalo in ga namestili na sodelovalnega robota Fanuc CR-7iA/L ter razvili spletno aplikacijo za sledenje in upravljanje sortiranja.

Keywords

magistrske naloge;globoko učenje;prepoznavanje objektov;YOLO;TACO;mehka robotika;sodelovalni roboti;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UL FS - Faculty of Mechanical Engineering
Publisher: [M. Čampelj]
UDC: 007.52:681.52:628.4(043.2)
COBISS: 218803715 Link will open in a new window
Views: 169
Downloads: 70
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Other data

Secondary language: English
Secondary title: Deep learning-based trash recognition and sorting system using soft robotics
Secondary abstract: The biggest challenge in waste sorting is the recognition and classification of waste, which can be done either directly, based on physical properties, or indirectly, using a camera. The latter usually involves the use of neural networks trained on a prepared training set to recognize objects in the image. We trained the YOLO algorithm on a publicly available dataset called TACO. For testing purposes, we created a test robotic cell by developing a soft pusher, mounting it on the collaborative robot Fanuc CR7iA/L and developed a web application for tracking and managing the sorting process.
Secondary keywords: master thesis;deep learning;object recognition;YOLO;TACO;soft robotics;collaborative robots;
Type (COBISS): Master's thesis/paper
Study programme: 0
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. Ljubljana, Fak. za strojništvo
Pages: XX, 59 str.
ID: 25001791
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