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Cattle detection and counting in UAV images based on convolutional neural networks / Wen Shao in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 1 (01 - 08 janvier 2020) . - pp 31 - 52 Titre : Cattle detection and counting in UAV images based on convolutional neural networks Type de document : Article/Communication Auteurs : Wen Shao, Auteur ; Rei Kawakami, Auteur ; Ryota Yoshihashi, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 31 - 52 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bovin [Termes IGN] chevauchement [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] comptage [Termes IGN] détection d'objet [Termes IGN] image captée par drone [Termes IGN] modélisation 3D Résumé : (auteur) For assistance with grazing cattle management, we propose a cattle detection and counting system based on Convolutional Neural Networks (CNNs) using aerial images taken by an Unmanned Aerial Vehicle (UAV). To improve detection performance, we take advantage of the fact that, with UAV images, the approximate size of the objects can be predicted when the UAV’s height from the ground can be assumed to be roughly constant. We resize an image to be fed into the CNN to an optimum resolution determined by the object size and the down-sampling rate of the network, both in training and testing. To avoid repetition of counting in images that have large overlaps to adjacent ones and to obtain the accurate number of cattle in an entire area, we utilize a three-dimensional model reconstructed by the UAV images for merging the detection results of the same target. Experiments show that detection performance is greatly improved when using the optimum input resolution with an F-measure of 0.952, and counting results are close to the ground truths when the movement of cattle is approximately stationary compared to that of the UAV’s. Numéro de notice : A2020-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2019.1624858 Date de publication en ligne : 11/06/2019 En ligne : https://doi.org/10.1080/01431161.2019.1624858 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94891 [article]