Object detection in refrigerators using Tensorflow

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dc.contributor.authorAgarwal, Kirti
dc.contributor.supervisorMuller, Hausi A.
dc.date.accessioned2019-01-02T17:49:24Z
dc.date.available2019-01-02T17:49:24Z
dc.date.copyright2018en_US
dc.date.issued2019-01-02
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractObject Detection is widely used in many applications such as face detection, detecting vehicles and pedestrians on streets, and autonomous vehicles. Object detection not only includes recognizing and classifying objects in an image, but also localizes those objects and draws bounding boxes around them. Therefore, most of the successful object detection networks make use of neural network based image classifiers in conjunction with object detection techniques. Tensorflow Object Detection API, an open source framework based on Google's TensorFlow, allows us to create, train and deploy object detection models. This thesis mainly focuses on detecting objects kept in a refrigerator. To facilitate the object detection in a refrigerator, we have used Tensorflow Object Detection API to train and evaluate models such as SSD-MobileNet-v2, Faster R-CNN-ResNet-101, and R-FCN-ResNet-101. The models are tested as a) a pre-trained model and b) a fine-tuned model devised by fine-tuning the existing models with a training dataset for eight food classes extracted from the ImageNet database. The models are evaluated on a test dataset for the same eight classes derived from the ImageNet database to infer which works best for our application. The results suggest that the performance of Faster R-CNN is the best on the test food dataset with a mAP score of 81.74%, followed by R-FCN with a mAP of 80.33% and SSD with a mAP of 76.39%. However, the time taken by SSD for detection is considerably less than the other two models which makes it a viable option for our objective. The results provide substantial evidence that the SSD model is the most suitable model for deploying object detection on mobile devices with an accuracy of 76.39%. Our methodology and results could potentially help other researchers to design a custom object detector and further enhance the precision for their datasets.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/10464
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectMachine Learningen_US
dc.subjectDeep Learningen_US
dc.subjectCNNen_US
dc.subjectImage Classificationen_US
dc.subjectObject Detectionen_US
dc.subjectTensorflow Object Detection APIen_US
dc.titleObject detection in refrigerators using Tensorflowen_US
dc.typeThesisen_US

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