In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of ...In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly,Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.展开更多
With the rise in drowning accidents in swimming pools,the demand for the precision and speed in artificial intelligence(AI)drowning detection methods has become increasingly crucial.Here,an improved YOLO-based method,...With the rise in drowning accidents in swimming pools,the demand for the precision and speed in artificial intelligence(AI)drowning detection methods has become increasingly crucial.Here,an improved YOLO-based method,named DrownACB-YOLO,for drowning detection in swimming pools is proposed.Since existing methods focus on the drowned state,a transition label is added to the original dataset to provide timely alerts.Following this expanded dataset,two improvements are implemented in the original YOLOv5.Firstly,the spatial pyramid pooling(SPP)module and the default upsampling operator are replaced by the atrous spatial pyramid pooling(ASPP)module and the content-aware reassembly of feature(CARAFE)module,respectively.Secondly,the cross stage partial bottleneck with three convolutions(C3)module at the end of the backbone is replaced with the bottleneck transformer(BotNet)module.The results of comparison experiments demonstrate that DrownACB-YOLO performs better than other models.展开更多
Purpose:The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images.Furthermore,this work presents an improved variant of the Tiny YOLOv7 model developed specifically fo...Purpose:The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images.Furthermore,this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection.The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.Design/methodology/approach:The approach adopted to carry out this work is twofold.Firstly,a richly annotated dataset consisting of eye disease classes,namely,cataract,glaucoma,retinal disease and normal eye,was created.Secondly,an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO.The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model.Moreover,at run time,the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results.Further,evaluations have been carried out for performance metrics,namely,precision,recall,F1 Score,average precision(AP)and mean average precision(mAP).Findings:The proposed EYE-YOLO achieved 28%higher precision,18%higher recall,24%higher F1 Score and 30.81%higher mAP than the Tiny YOLOv7 model.Moreover,in terms of AP for each class of the employed dataset,it achieved 9.74%higher AP for cataract,27.73%higher AP for glaucoma,72.50%higher AP for retina disease and 13.26%higher AP for normal eye.In comparison to the state-of-the-art Tiny YOLOv5,Tiny YOLOv6 and Tiny YOLOv8 models,the proposed EYE-YOLO achieved 6:23.32%higher mAP.Originality/value:This work addresses the problem of eye disease recognition as a bounding box regression and detection problem.Whereas,the work in the related research is largely based on eye disease classification.The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors.The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection.The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.展开更多
基金supported by the National Natural Science Foundation of China(71401052)the Fundamental Research Funds for the Central Universities(2019B19514)。
文摘In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly,Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.
文摘With the rise in drowning accidents in swimming pools,the demand for the precision and speed in artificial intelligence(AI)drowning detection methods has become increasingly crucial.Here,an improved YOLO-based method,named DrownACB-YOLO,for drowning detection in swimming pools is proposed.Since existing methods focus on the drowned state,a transition label is added to the original dataset to provide timely alerts.Following this expanded dataset,two improvements are implemented in the original YOLOv5.Firstly,the spatial pyramid pooling(SPP)module and the default upsampling operator are replaced by the atrous spatial pyramid pooling(ASPP)module and the content-aware reassembly of feature(CARAFE)module,respectively.Secondly,the cross stage partial bottleneck with three convolutions(C3)module at the end of the backbone is replaced with the bottleneck transformer(BotNet)module.The results of comparison experiments demonstrate that DrownACB-YOLO performs better than other models.
基金supported by the International Research Center of Big Data for Sustainable Development Goals [grant number CBAS2022GSP01]the National Natural Science Foundation of China [grant numbers 42276203 and 42030406]+1 种基金the Natural Science Foundation of Shandong Province [grant number ZR2021MD001]the Laoshan Laboratory [grant number LSKJ202204302].
文摘Purpose:The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images.Furthermore,this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection.The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.Design/methodology/approach:The approach adopted to carry out this work is twofold.Firstly,a richly annotated dataset consisting of eye disease classes,namely,cataract,glaucoma,retinal disease and normal eye,was created.Secondly,an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO.The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model.Moreover,at run time,the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results.Further,evaluations have been carried out for performance metrics,namely,precision,recall,F1 Score,average precision(AP)and mean average precision(mAP).Findings:The proposed EYE-YOLO achieved 28%higher precision,18%higher recall,24%higher F1 Score and 30.81%higher mAP than the Tiny YOLOv7 model.Moreover,in terms of AP for each class of the employed dataset,it achieved 9.74%higher AP for cataract,27.73%higher AP for glaucoma,72.50%higher AP for retina disease and 13.26%higher AP for normal eye.In comparison to the state-of-the-art Tiny YOLOv5,Tiny YOLOv6 and Tiny YOLOv8 models,the proposed EYE-YOLO achieved 6:23.32%higher mAP.Originality/value:This work addresses the problem of eye disease recognition as a bounding box regression and detection problem.Whereas,the work in the related research is largely based on eye disease classification.The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors.The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection.The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.
