Combining frequency information and the unsupervisedW-Net model for wheat head detection

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.