In the e-commerce industry, importing data from third party clothing brands require validation of this data. If the validation step of this data is done manually, it is a tedious and time-consuming task. Part of this task can be replaced or assisted by using computer vision to automatically find clothing types, such as T-shirts and pants, within imported images. After a detection of clothing type is computed, it is possible to recommend the likelihood of clothing products correlating to data imported with a certain accuracy. This was done alongside a prototype interface that can be used to start training, finding clothing types in an image and to mask annotations of products. Annotations are areas describing different clothing types and are used to train an object detector model. A model for finding clothing types is trained on Mask R-CNN object detector and achieves 0.49 mAP accuracy. A detection take just above one second on an Nvidia GTX 1070 8 GB graphics card. Recommending one or several products based on a detection take 0.5 seconds and the algorithm used is k-nearest neighbors. If prediction is done on products of which is used to build the model of the prediction algorithm almost perfect accuracy is achieved while products in images for another products does not achieve nearly as good results.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-75235 |
| Date | January 2019 |
| Creators | Nilsson Harnert, Henrik |
| Publisher | Luleå tekniska universitet, Datavetenskap |
| 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 |
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