Camouflage Object Segmentation Using an Optimized Deep-Learning Approach

Muhammad Kamran, Saeed Ur Rehman (), Talha Meraj, Khalid A. Alnowibet and Hafiz Tayyab Rauf ()
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Muhammad Kamran: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Rawalpindi 47040, Pakistan
Saeed Ur Rehman: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Rawalpindi 47040, Pakistan
Talha Meraj: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Rawalpindi 47040, Pakistan
Khalid A. Alnowibet: Statistics and Operations Research Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
Hafiz Tayyab Rauf: Independent Researcher, Bradford BD8 0HS, UK

Mathematics, 2022, vol. 10, issue 22, 1-20

Abstract: Camouflage objects hide information physically based on the feature matching of the texture or boundary line within the background. Texture matching and similarities between the camouflage objects and surrounding maps make differentiation difficult with generic and salient objects, thus making camouflage object detection (COD) more challenging. The existing techniques perform well. However, the challenging nature of camouflage objects demands more accuracy in detection and segmentation. To overcome this challenge, an optimized modular framework for COD tasks, named Optimize Global Refinement (OGR), is presented. This framework comprises a parallelism approach in feature extraction for the enhancement of learned parameters and globally refined feature maps for the abstraction of all intuitive feature sets at each extraction block’s outcome. Additionally, an optimized local best feature node-based rule is proposed to reduce the complexity of the proposed model. In light of the baseline experiments, OGR was applied and evaluated on a benchmark. The publicly available datasets were outperformed by achieving state-of-the-art structural similarity of 94%, 93%, and 96% for the Kvasir-SEG, COD10K, and Camouflaged Object (CAMO) datasets, respectively. The OGR is generalized and can be integrated into real-time applications for future development.

Keywords: semantic segmentation; global refinement; camouflage objects; graph fusion; edge enhancement; boundary guidance; graph convolutional network; vision transformer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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