The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has signifi...The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty quantification.Therefore,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the predictions.It prepares convolutional neural network(CNN)and BCNN models.The proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN model.This study then trains them on a dataset of cataract images filtered from the ocular disease fundus images fromKaggle.The deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye conditions.When compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.展开更多
Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex backgrounds.Traditional methods,while effective in controlled environments,...Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex backgrounds.Traditional methods,while effective in controlled environments,often fail in scenarios involving long-range targets,high noise levels,or intricate backgrounds,highlighting the need for more robust approaches.To address these challenges,we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target saliency.This framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty maps.By utilizing uncer-tainty predictions,our method refines segmentation outcomes,achieving superior detection accuracy.Notably,this marks the first application of uncertainty modeling within the context of infrared UAV target detection.Experimental evaluations on three publicly available infrared UAV datasets demonstrate the effectiveness of the proposed framework.The results reveal significant improvements in both detection precision and robustness when compared to state-of-the-art deep learning models.Our approach also extends the capabilities of encoder-decoder convolutional neural networks by introducing uncertainty modeling,enabling the network to better handle the challenges posed by small targets and complex environmental conditions.By bridging the gap between theoretical uncertainty modeling and practical detection tasks,our work offers a new perspective on enhancing model interpretability and performance.The codes of this work are available openly at https://github.com/general-learner/UQ_Anti_UAV(acceessed on 11 November 2024).展开更多
基金Saudi Arabia for funding this work through Small Research Group Project under Grant Number RGP.1/316/45.
文摘The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty quantification.Therefore,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the predictions.It prepares convolutional neural network(CNN)and BCNN models.The proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN model.This study then trains them on a dataset of cataract images filtered from the ocular disease fundus images fromKaggle.The deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye conditions.When compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.
基金supported by the Science and Technology Project of Sichuan(Grant No.2024ZHCG0170)the National Key Research and Development Program of China,“Key Technologies for Instrumentation and Control System Program Security Based on Blockchain”(Project No.2024YFB3311000)+1 种基金States Key Laboratory of Air Traffic Management System(Grant No.SKLATM202202)the Chengdu Science and Technology Project(Grant No.2022-YF05-00068-SN).
文摘Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex backgrounds.Traditional methods,while effective in controlled environments,often fail in scenarios involving long-range targets,high noise levels,or intricate backgrounds,highlighting the need for more robust approaches.To address these challenges,we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target saliency.This framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty maps.By utilizing uncer-tainty predictions,our method refines segmentation outcomes,achieving superior detection accuracy.Notably,this marks the first application of uncertainty modeling within the context of infrared UAV target detection.Experimental evaluations on three publicly available infrared UAV datasets demonstrate the effectiveness of the proposed framework.The results reveal significant improvements in both detection precision and robustness when compared to state-of-the-art deep learning models.Our approach also extends the capabilities of encoder-decoder convolutional neural networks by introducing uncertainty modeling,enabling the network to better handle the challenges posed by small targets and complex environmental conditions.By bridging the gap between theoretical uncertainty modeling and practical detection tasks,our work offers a new perspective on enhancing model interpretability and performance.The codes of this work are available openly at https://github.com/general-learner/UQ_Anti_UAV(acceessed on 11 November 2024).
