Print Email Facebook Twitter Combining frequency information and the unsupervisedW-Net model for wheat head detection Title Combining frequency information and the unsupervisedW-Net model for wheat head detection Author Chen, Ivo (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Lengyel, A. (mentor) Pintea, S. (graduation committee) Isufi, E. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2021-07-01 Abstract Wheat is a widely used ingredient for food products. To increase the productionand quality of wheat, the density of ’wheat heads’ in a farm can be studied. Accuratelylocating wheat heads in images can be challenging. A lot of work has taken place insupervised semantic segmentation, but these networks typically require large pixel-wisehuman-annotated labeled data. Gathering this data is tedious and labour intensive.This paper proposes to use the novel unsupervised semantic segmentation model W-Net to solve this problem. To improve the accuracy, we investigated the influence ofthe frequency domain, by pre-processing the training data two different times using acustom filter, based on frequencies found in wheat heads, and a high pass filter.The approach is evaluated on the Global Wheat Head Detection (GWHD) dataset[11]. To compare the accuracy the generated segmentations were mapped to boundingboxes based. The proposed method did not show to be able to generate competing de-tection compared to the baseline method associated with the GWHD dataset, but theGWHD dataset has a different measurement of truth, consisting out bounding boxesinstead of segments which is in the disadvantage for the W-Net.Pre-processing the dataset using the high pass filter did increase the intersection overunion with 1,4% and the deviation of the reconstruction loss was smaller when fre-quency filtering was applied.Although the object detection has a low accuracy, this study showed that some ba-sic wheat head detection can be achieved by using the unsupervised segmentationmethod W-Net and the accuracy can be increased if a high pass filter is applied aspre-processing step. Subject W-Netwheat head detectionUnsupervisedFrequency domain To reference this document use: http://resolver.tudelft.nl/uuid:c49c37b2-66c8-4477-a82b-2194af6aaac1 Part of collection Student theses Document type bachelor thesis Rights © 2021 Ivo Chen Files PDF cciachen_reseach_project_ ... nal_v1.pdf 6.6 MB Close viewer /islandora/object/uuid:c49c37b2-66c8-4477-a82b-2194af6aaac1/datastream/OBJ/view