MarsNet: Multi-Label Classification Network for Images of Various Sizes

Since the Convolutional Neural Network (CNN) has surfaced and fascinated the world, many researchers have exploited CNN for image classification, object detection, semantic segmentation, etc. However, the conventional CNNs have a pyramidal structure and were designed to process images which have the...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.21832-21846
Main Authors: Park, Ju-Youn, Hwang, Yewon, Lee, Dukyoung, Kim, Jong-Hwan
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Convolutional neural networks
Datasets
Defects
Feature maps
Image classification
Image segmentation
images of various sizes
Inspection
Modules
multi-label classification
Object recognition
Performance degradation
printed circuit board
solder paste inspection
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Since the Convolutional Neural Network (CNN) has surfaced and fascinated the world, many researchers have exploited CNN for image classification, object detection, semantic segmentation, etc. However, the conventional CNNs have a pyramidal structure and were designed to process images which have the same size. Although some CNNs can accept images of various sizes, performance is degraded for images smaller than the size of images used for training. In this paper, we propose MarsNet, a CNN based end-to-end network for multi-label classification with an ability to accept various size inputs. In order to allow the network to accept such images, dilated residual network (DRN) is modified to get higher resolution feature maps, and horizontal vertical pooling (HVP) is newly designed to efficiently aggregate positional information from the feature maps. Furthermore, multi-label scoring module and threshold estimation module are employed to serve the purpose of multi-label classification. We verify the effectiveness of the proposed network through two distinctive experiments. We first verify our model by inspecting and classifying multiple types of defects occurred in PCB screen printer using solder paste inspection (SPI) datasets. Secondly, we verify our network using VOC 2007 dataset. Our network is pioneering in that no research has attempted to accomplish multi-label classification for defects in addition to being able to take input images of various sizes in SPI field.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2969217