Machine learning algorithms have had breakthroughs in many areas in the last decades. The hardest task, to solve with machine learning, was solving tasks that humans solve intuitively, e.g. understanding natural language or recognizing specific objects in images. To overcome these problems is to allow the computer to learn from experience, instead of implementing a pre-written program to solve the problem at hand - that is how Neural Networks came to be. Neural Network is widely used in image analysis, and object detection algorithms have evolved considerably in the last years. Two of these algorithms are Faster Region-basedConvolutional Neural Networks(Faster R-CNN) and You Only Look Once(YOLO). The purpose of this thesis is to evaluate and benchmark state-of-the-art object detection methods and then analyze their performance based on reading information from images. The information that we aim to extract is digital and analog digits from the odometer of a car, this will be done through object recognition and region-based image analysis. Our models will be compared to the open-source Optical Character Recognition(OCR) model Tesseract, which is in production by the Stockholm-based company Greater Than. In this project we will take a more modern approach and focus on two object detection models, Faster R-CNN and YOLO. When training these models, we will use transfer learning. This means that we will use models that are pre-trained, in our case on a dataset called ImageNet, specifically for object detection. We will then use the TRODO dataset to train these models further, this dataset consists of 2 389 images of car odometers. The models are then evaluated through the measures of mean average precision(mAP), prediction accuracy, and Levenshtein Distance. Our findings are that the object detection models are out-performing Tesseract for all measurements. The highest mAP and accuracy is attained by Faster R-CNN while the best results, regarding Levenshtein distance, are achieved by a YOLO model. The final result is clear, both of our approaches have more diversity and are far better thanTesseract, for solving this specific problem.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-481952 |
| Date | January 2022 |
| Creators | Hjelm, Mandus, Andersson, Eric |
| Publisher | Uppsala universitet, Institutionen för informationsteknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC STS, 1650-8319 ; 22009 |
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