Coarse-to-fine salient object detection based on deep convolutional neural networks

2018 ◽  
Vol 64 ◽  
pp. 21-32 ◽  
Author(s):  
Ying Li ◽  
Fan Cui ◽  
Xizhe Xue ◽  
Jonathan Cheung-Wai Chan
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Salient Object Detection ◽  
Salient Object ◽  
Deep Convolutional Neural Networks ◽  
Coarse To Fine

scholarly journals Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Qimei Wang ◽  
Feng Qi ◽  
Minghe Sun ◽  
Jianhua Qu ◽  
Jie Xue
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Detection Methods ◽  
Disease Detection ◽  
Deep Convolutional Neural Networks ◽  
Detection Techniques ◽  
Tomato Diseases ◽  
Validation Set ◽  
Tomato Disease

This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.

scholarly journals Global Context-Aware Progressive Aggregation Network for Salient Object Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 10599-10606 ◽  
Author(s):  
Zuyao Chen ◽  
Qianqian Xu ◽  
Runmin Cong ◽  
Qingming Huang
Keyword(s):  
Object Detection ◽  
Critical Role ◽  
Saliency Map ◽  
Salient Object Detection ◽  
Semantic Features ◽  
Context Aware ◽  
Salient Object ◽  
Deep Convolutional Neural Networks ◽  
Global Context ◽  
High Level

Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiple-level feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.

Salient object detection using coarse-to-fine processing

2017 ◽  
Vol 34 (3) ◽  
pp. 370 ◽  
Author(s):  
Qiangqiang Zhou ◽  
Lin Zhang ◽  
Weidong Zhao ◽  
Xianhui Liu ◽  
Yufei Chen ◽  
...  
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Coarse To Fine

FOREIGN OBJECT DETECTION IN TV AND LWIR IMAGERY OF A RUNWAY BY DEEP CONVOLUTIONAL NEURAL NETWORKS

2017 ◽  
Vol 9 (4) ◽  
pp. 89-96
Author(s):  
V.V. Kniaz ◽  
V.V. Fedorenko ◽  
V.A. Mizginov ◽  
V.A. Knyaz ◽  
W. Purgathofer
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Foreign Object ◽  
Deep Convolutional Neural Networks

Coarse to Fine: Weak Feature Boosting Network for Salient Object Detection

2020 ◽  
Vol 39 (7) ◽  
pp. 411-420
Author(s):  
Chenhao Zhang ◽  
Shanshan Gao ◽  
Xiao Pan ◽  
Yuting Wang ◽  
Yuanfeng Zhou
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Coarse To Fine
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