Examinando por Autor "Fu, L."

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    Deep Learning based flower detection and counting in highly populated images: A peach grove case study
    (Elsevier, 2024-03) Estrada, J.; Vasconez, J.; Fu, L.; Cheein, F.
    Farmers and producers need to estimate crop yield in order to plan and allocate human and economic resources during the harvesting season. For many crops, such as peach groves, the number of fruits is correlated with the number of flowers produced by each tree. Therefore, estimating the number of flowers in peach groves can serve as a good indicator of crop yield, disregarding climate hazards. However, in peach groves, tree images present several challenges, including a high number of flowers, interference from distant trees, and occlusion between elements. These issues pose a difficult task for computer vision and machine learning techniques. In this study, we propose the utilization of state-of-the-art deep learning techniques for image detection purposes; namely the YOLO architectures on its versions 5, 7, and 8 and their different size models (n, s, m, l, x); as well as predicting object density using multi-column in densely populated images, using a multi-column deep neural network. The methodology was tested on a new dataset comprising 600 images of peach trees during the blooming season, in the region of Catalonia, Spain. Out of these, 400 images were used to train the model, while 100 were allocated for testing and another 100 for validation. The counting results were evaluated using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and percentage error (%Err). For the detection algorithms, metrics such as accuracy, precision, recall, and mean average precision were utilized, alongside metrics for evaluating the counting process. The experiments demonstrated that predicting the density map yielded better results in the counting process, achieving an MAE of 39.13, RMSE of 69.69, and a percentage error of 9.98. The detection algorithm that exhibited superior performance was YOLOv7x, with metrics of MAE 152.7, RMSE 212.9, and a percentage error of 29.7 %. These results indicate that, for counting purposes, predicting the density map produced better overall outcomes.
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    Weak-lensing study in VOICE survey - I. Shear measurement
    (Oxford University Press, 2018-09) Fu, L.; Liu, D.; Radovich, M.; Liu, X.; Pan, C.; Fan, Z.; Covone, G.; Vaccari, M.; Amaro, V.; Brescia, M.; Capaccioli, M.; De Cicco, D.; Grado, A.; Limatola, L.; Miller, L.; Napolitano, N.R.; Paolillo, M.; Pignata, G.
    The VST Optical Imaging of the CDFS and ES1 Fields (VOICE) Survey is a Guaranteed Time programme carried out with the European Southern Observatory (ESO) VLT Survey Telescope (VST) telescope to provide deep optical imaging over two 4 deg2 patches of the sky centred on the Chandra Deep Field South (CDFS) and ES1 as part of the ESO-Spitzer Imaging Extragalactic Survey. We present the cosmic shear measurement over the 4 deg2 covering the CDFS region in the r band using LensFit. Each of the four tiles of 1 deg2 has more than 100 exposures, of which more than 50 exposures passed a series of image quality selection criteria for weak-lensing study. The 5σ limiting magnitude in r band is 26.1 for point sources, which is ≳1 mag deeper than other weak-lensing survey in the literature [e.g. the Kilo Degree Survey (KiDS) at VST]. The photometric redshifts are estimated using the VOICE u, g, r, i together with near-infrared VIDEO data Y, J, H, Ks. The mean redshift of the shear catalogue is 0.87, considering the shear weight. The effective galaxy number density is 16.35 gal arcmin-2, which is nearly twice the one of KiDS. The performance of LensFit on such a deep data set was calibrated using VOICE-like mock image simulations. Furthermore, we have analysed the reliability of the shear catalogue by calculating the star-galaxy crosscorrelations, the tomographic shear correlations of two redshift bins and the contaminations of the blended galaxies. As a further sanity check, we have constrained cosmological parameters by exploring the parameter space with Population Monte Carlo sampling. For a flat Λ cold dark matter model, we have obtained Σ8 = σ8(Ωm/0.3)0.5 = 0.68+0.11 -0.15. © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.