Authors:
Paweł Majewski
1
;
Piotr Lampa
2
;
Robert Burduk
1
and
Jacek Reiner
2
Affiliations:
1
Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Poland
;
2
Faculty of Mechanical Engineering, Wrocław University of Science and Technology, Poland
Keyword(s):
Pseudo-Labelling, Spatio-Temporal, Optical Flow, Object Detection, Insect, Monitoring, Tenebrio Molitor.
Abstract:
Pest detection is an important application problem as it enables early reaction by the farmer in situations of unacceptable pest infestation. Developing an effective pest detection model is challenging due to the problem of creating a representative dataset, as episodes of pest occurrence under real rearing conditions are rare. Detecting the pest Alphitobius diaperinus Panzer in mealworm (Tenebrio molitor) rearing, addressed in this work, is particularly difficult due to the relatively small size of detection objects, the high similarity between detection objects and background elements, and the dense scenes. Considering the problems described, an original method for developing pest detection models was proposed. The first step was to develop a basic model by training it on a small subset of manually labelled samples. In the next step, the basic model identified low/moderate pest-infected rearing boxes from many boxes inspected daily. Pseudo-labelling was carried out for these boxes,
significantly reducing labelling time, and re-training was performed. A spatio-temporal masking method based on activity maps calculated using the Gunnar-Farneback optical flow technique was also proposed to reduce the numerous false-positive errors. The quantitative results confirmed the positive effect of pseudo-labelling and spatio-temporal masking on the accuracy of pest detection and the ability to recognise episodes of unacceptable pest infestation.
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