LAUSR

Abstract. With the advances in remote sensing, wild animals sprawling over a vast area can be easily and quickly captured using low-cost unmanned aerial vehicle imagery. We propose an aerial animal detection and counting network (DetCountNet) framework called FSSCaps-DetCountNet, using fuzzy soft sets (FSS) and capsule network (CapsNet). Similarity measures based on FSS have been used to discriminate the target animals from both nontargets and the background. Of particular interest to aerial images, CapsNet requires very few training data and is robust to rotation and affine transformation. With superpixel segmentation and attention maps, FSSCaps-DetCountNet works well on challenging image conditions, such as dense background with sparse animals and overlapping/cluttered animals. The model is trained and tested on benchmark aerial animal datasets, namely, the aerial elephants and the livestock datasets with an accuracy index of 99.84% and 99.86%, respectively. Also, the overall omission and commission errors are 0.02% and 0.03%, respectively. The experimental results and comparative study with other state-of-the-art conventional models demonstrate the effectiveness and robustness of FSSCaps-DetCountNet for real-time animal detection and counting from aerial images.