Many deep learning-based solutions proposed to automate the analysis
of camera-trap images for animal counting and behaviour detection
deployed image classifiers that produce image-level labels, tackle
animal counting as a classification task, and use images with one
animal or attribute. They also used large image datasets which are
costly, time-consuming and laborious to collect and annotate, not
feasible for rare/elusive species and resource-constrained projects and
did not explain the generalization of the models on images with
untrained image backgrounds. This study developed animal counting
and behaviour detection models for wild animals using You-Only-
Look-Once (YOLO) and small-sized datasets with 20,110 camera-trap
images. The study results show that using appropriate technologies
including transfer learning, data augmentation and efficient data
splitting methods, it is feasible to develop high-accurate and location-
invariant object detection models using small-sized image datasets.
Despite high performance, the animal counting model did not perform
well on crowding/interacting animals including Guineafowl, Elephant,
Lion, Zebra, Wildebeest, Baboon and Giraffe and it misclassified a
significant number of Wildebeest as Buffalo and Zebra, but few
Buffalo and Zebra were misclassified as Wildebeest. The behaviour
detection model performed well on all behaviours except interacting.
Model’s poor performance on interacting is ascribed to its messy and
small training set compared to other behaviour classes. The selection
of an optimal confidence threshold appropriate for a particular dataset
and increasing data diversity of the training set can significantly
improve recall while reducing false positives.
Frank G. Kilima1, Shubi Kaijage2, Edith Luhanga3, Colin Torney4
Nelson Mandela African Institution of Science and Technology, Tanzania1,2,3, University of Glasgow, Scotland4
Artificial neural network (ANN), Convolutional neural network (CNN), Deep learning, Generalization, Object detection, You-Only- Look-Once (YOLO)
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 15 , Issue: 4 , Pages: 3722 - 3728 )
Date of Publication :
January 2025
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