Abstract
The evolution in imaging technologies and artificial intelligence algorithms, coupled with improvements in UAV technology, has enabled the use of unmanned aircraft in a wide range of applications. The feasibility of this kind of approach for cattle monitoring has been demonstrated by several studies, but practical use is still challenging due to the particular characteristics of this application, such as the need to track mobile targets and the extensive areas that need to be covered in most cases. The objective of this study was to investigate the feasibility of using a tilted angle to increase the area covered by each image. Deep Convolutional Neural Networks (Xception architecture) were used to generate the models for animal detection. Three experiments were carried out: (1) five different sizes for the input images were tested to determine which yields the highest accuracies; (2) detection accuracies were calculated for different distances between animals and sensor, in order to determine how distance influences detectability; and (3) animals that were completely missed by the detection process were individually identified and the cause for those errors were determined, revealing some potential topics for further research. Experimental results indicate that oblique images can be successfully used under certain conditions, but some practical limitations need to be addressed in order to make this approach appealing.
Highlights
The management of beef cattle farms operating under an extensive production system is challenging, especially considering that many of those farms have large areas with deficient communications infrastructure and ground access
With very few exceptions [9], the information contained in the images is extracted by means of deep learning models, using one of four main approaches [1]: semantic segmentation, which associates each pixel in the image to a class; instance segmentation, which detects and delineates each distinct object of interest [4,5]; object detection, which delineates a box bounding the objects of interest [10,11]; and heat mapping using Convolutional Neural
This article explores the possibility of using tilted angles to increase the area covered by a single image captured using Unmanned aerial vehicles (UAV)
Summary
The management of beef cattle farms operating under an extensive production system is challenging, especially considering that many of those farms have large areas with deficient communications infrastructure and ground access. Under those conditions, thorough visual inspection of the herd often requires manned flight, which is expensive and has some risks associated [1]. The idea is to use UAVs to capture a large number of images from a certain area, and use algorithms to extract the information of interest. With very few exceptions [9], the information contained in the images is extracted by means of deep learning models, using one of four main approaches [1]: semantic segmentation, which associates each pixel in the image to a class; instance segmentation, which detects and delineates each distinct object of interest [4,5]; object detection, which delineates a box bounding the objects of interest [10,11]; and heat mapping (probability distributions) using Convolutional Neural
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