EDRNet: Encoder-Decoder Residual Network for Salient Object Detection of Strip Steel Surface Defects

It is still a challenging task to detect the surface defects of strip steel due to its complex variations, including variable defect types, cluttered background, low contrast, and noise interference. The existing detection methods cannot effectively segment the defect objects from complex background...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 69; H. 12; S. 9709 - 9719
Hauptverfasser:	Song, Guorong, Song, Kechen, Yan, Yunhui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Anomaly detection Artificial neural networks Background noise Coders Encoder–Decoder Feature extraction Object recognition Residual neural networks residual refinement structure Salience Saliency detection salient object detection Segments Steel Strip steel Surface defects
ISSN:	0018-9456, 1557-9662
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!