Results 81 to 90 of about 7,417,847 (369)

A deep convolutional neural network for diabetic retinopathy detection via mining local and long‐range dependence

CAAI Transactions on Intelligence Technology
Diabetic retinopathy (DR), the main cause of irreversible blindness, is one of the most common complications of diabetes. At present, deep convolutional neural networks have achieved promising performance in automatic DR detection tasks.
Xiaoling Luo, Wei Wang, Yong Xu, Zhihui Lai, Xiaopeng Jin, Bob Zhang, David Zhang   +6 more
doaj   +1 more source

An Adaptive Network for Arbitrary-Oriented and Small Object Detection in Remote Sensing Images

IEEE Access
To address the challenges posed by detection of arbitrary orientation object and small object in remote sensing imagery, this paper proposes an innovative object detection model based on YOLOv8, named YOLOv8-DFS.
Zhiyong Zeng, Chao Wu, Wenqi Zeng
doaj   +1 more source

Soft-NMS -- Improving Object Detection With One Line of Code

, 2017
Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box M with the maximum score is selected and all other detection boxes with a significant ...
Bodla, Navaneeth, Chellappa, Rama, Davis, Larry S., Singh, Bharat   +3 more
core   +1 more source

Contextualizing Object Detection and Classification [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
We investigate how to iteratively and mutually boost object classification and detection performance by taking the outputs from one task as the context of the other one. While context models have been quite popular, previous works mainly concentrate on co-occurrence relationship within classes and few of them focus on contextualization from a top-down ...
Chen, Qiang, Song, Zheng, Dong, Jian, Huang, Zhongyang, Hua, Yang, Yan, Shuicheng   +5 more
openaire   +4 more sources

Nutritional and Behavioral Intervention for Long‐Term Childhood Acute Leukemia Survivors With Metabolic Syndrome

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Purpose Metabolic syndrome (MetS) is a common complication in survivors of childhood acute lymphoblastic and myeloid leukemia (AL), and a major risk factor for premature cardiovascular disease, type‐2‐diabetes, and metabolic dysfunction‐associated steatotic liver disease (MASLD).
Visentin Sandrine, Béliard Sophie, Renard Cécile, Caussy Cyrielle, Hamidou Zeinab, Romano David, Gacem Mélanie, Petit Arnaud, Saultier Paul, Borentain Patrick, Michel Gérard   +10 more
wiley   +1 more source

Survey of Adversarial Attack and Defense for RBG and Infrared Multimodal Object Detection [PDF]

Jisuanji kexue
Object detection,as a fundamental classic task in the field of computer vision,has a wide range of applications.Deep learning based object detection algorithms have become the mainstream of current research due to their superior performance.However,most ...
ZHENG Haibin, LIN Xiuhao, CHEN Jingwen, CHEN Jinyin
doaj   +1 more source

Auxiliary Detection Head for One-Stage Object Detection

IEEE Access, 2020
The auxiliary classifier can improve the performance of classification networks. However, the utility of the auxiliary detection head has not been explored in the object detection field.
Guozheng Jin, Rin-Ichiro Taniguchi, Fengzhong Qu   +2 more
doaj   +1 more source

RGBD Salient Object Detection, Based on Specific Object Imaging

Sensors, 2022
RGBD salient object detection, based on the convolutional neural network, has achieved rapid development in recent years. However, existing models often focus on detecting salient object edges, instead of objects.
Xiaolian Liao, Jun Li, Leyi Li, Caoxi Shangguan, Shaoyan Huang   +4 more
doaj   +1 more source

Pseudo Mask Augmented Object Detection

, 2018
In this work, we present a novel and effective framework to facilitate object detection with the instance-level segmentation information that is only supervised by bounding box annotation.
Liang, Shuang, Wei, Yichen, Zhao, Xiangyun   +2 more
core   +1 more source

Towards Dependable Object Detection

IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, 2020
A high confidence in object detection is very crucial for object detector modules to be used in real world applications. Though the confidence scores in object detection can be improved by using better and larger training data set and using more robust architectures, which is an approach from the computer vision side, this paper aims to improve the ...
Muthuchamy Selvaraj, Nithish, Muhammad, Ilyas, Cheah, Chien Chern   +2 more
openaire   +2 more sources