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Comparison Of Object Detection Models - to detect recycle logos on tetra packs
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2022 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Background: Manufacturing and production of daily used products using recyclable materials took a steep incline over the past few years. The recyclable packages that are being considered for this thesis are Tetra Packs. Tetra packs are widely used for packaging liquid foods. A few recyclable methods are being used to recycle such tetra packs which use the barcode behind them to scan and give which recyclable method the particular tetra pack has to go through. In some cases, the barcode might get worn off due to excessive usage leading to a problem. Therefore there needs to be a research that has to be carried out to address this problem and find a solution to the same. 

Objectives: The objectives to address and fulfill the aim of this thesis are : To find/create the necessary data set containing clear pictures of the tetra packs with visible recyclable logos. To draw bounding boxes around the objects i.e., logos for training the models. To test the data set by applying all four Deep Learning models. To compare each of the models on speed and the performance metrics i.e, mAP and IoU and identify the best algorithm among them. 

Methods: To answer the research question we have chosen one research methodol- ogy which is Experiment.Results: YOLOv5 is considered as the best algorithm among the four algorithms we are comparing. Speed of YOLOv5, SSD and Faster-RCNN were found to be similar i.e, 0.2 seconds whereas Mask-RCNN was the slowest with the detection speed of 1.0 seconds. The mAP score of SSD is 0.86 which is the highest among the four followed by YOLOv5 at 0.771, Faster-RCNN at 0.67 and Mask-RCNN at 0.62. IoU score of Faster-RCNN is 0.96 which is the highest among the four followed by YOLOv5 at 0.95, SSD at 0.50 and Mask-RCNN at 0.321. On comparing all the above results YOLOv5 is concluded as the best algorithm among the four as it is relatively fast and accurate without any major draw-backs in any category. 

Conclusions: Amongst the four algorithms Faster-RCNN, YOLO, SSD and Mask- RCNN, YOLOv5 is declared as the best algorithm after comparing all the models based on speed and the performance metrics mAP, IoU. YOLOv5 is considered as the best algorithm among the four algorithms we are comparing. 

Place, publisher, year, edition, pages
2022.
Keywords [en]
Object Detection, Deep Learning, Convolutional Neural Networks.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-23390OAI: oai:DiVA.org:bth-23390DiVA, id: diva2:1679144
Subject / course
DV1478 Bachelor Thesis in Computer Science
Educational program
DVGDT Bachelor Qualification Plan in Computer Science 60.0 hp
Presentation
2022-05-27, Campus Grasvik, Karlskrona, 15:00 (English)
Supervisors
Examiners
Available from: 2022-06-30 Created: 2022-06-30 Last updated: 2022-06-30Bibliographically approved

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