An Efficient Contour Based Fine-Grained Algorithm for Multi Category Object Detection

2018 ◽  
Vol 6 (2) ◽  
pp. 127-136
Author(s):  
Rafflesia Khan ◽  
◽  
Tarannum Fariha Raisa ◽  
Rameswar Debnath
Keyword(s):  
Object Detection ◽  
Fine Grained

Efficient Fine-Grained Object Detection for Robot-Assisted WEEE Disassembly

2020 ◽  
pp. 17-22
Author(s):  
Ioannis Athanasiadis ◽  
Athanasios Psaltis ◽  
Apostolos Axenopoulos ◽  
Petros Daras
Keyword(s):  
Object Detection ◽  
Robot Assisted ◽  
Fine Grained

Oriented Object Detection with Fine-Grained Enhancement and Angle Constraint

2021 ◽  
Author(s):  
Mengyuan Wang ◽  
Xuanyu Zhang ◽  
Chuanbo Yu ◽  
Tingyi Guo ◽  
Jingxiao Gu ◽  
...  
Keyword(s):  
Object Detection ◽  
Fine Grained ◽  
Oriented Object

scholarly journals Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

2020 ◽  
Vol 12 (18) ◽  
pp. 3053 ◽  
Author(s):  
Thorsten Hoeser ◽  
Felix Bachofer ◽  
Claudia Kuenzer
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Object Detection ◽  
Land Surface ◽  
Large Scale ◽  
Earth Observation ◽  
Observation Data ◽  
Fine Grained ◽  
The Impact ◽  
Object Dynamics

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.

scholarly journals FINE-GRAINED OBJECT DETECTION

2018 ◽  
Author(s):  
Rahul Dalal
Keyword(s):  
Object Detection ◽  
Fine Grained
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