Camouflage object segmentation based on feature extraction and mixed attention

ZeYao Zhang; Li Cui

doi:10.1117/12.2679267

25 May 2023 Camouflage object segmentation based on feature extraction and mixed attention

ZeYao Zhang, Li Cui

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127120T (2023) https://doi.org/10.1117/12.2679267
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

In recent years, the technology of camouflage semantic segmentation using machine learning and computer vision has been widely concerned as one of the emerging research hotspots in the field of artificial intelligence. There are several methods have been developed for camouflage object segmentation, which still can be improved to solve the existing problems such as incomplete segmentation, edge dispersion, and too many parameters. This paper proposes a lightweight camouflage segmentation model called ConvNeXt Orientation Aggregation net (COA-net). In this paper, the camouflage segmentation model PFnet is selected as the basic network, and the feature extraction ability is enhanced by using the lightweight backbone network ConvNeXt. Futhermore, a plug-and-play mixed attention mechanism, called CDSEnet, is proposed, which enhances the ability to identify effective features and interference information. The experimental results show that the proposed method can achieve better segmentation performance compared with the original model. The experimental results show the proposed method can achieve the improvement of the index structure measure Sα, the adaptive measure Eφ, the weighted measure Fβ and the average absolute error M by 3.3%, 2.7%, 6.3% and 20.1% respectively and the subjective quality is better as well compared with the original method.

Citation Download Citation

ZeYao Zhang and Li Cui "Camouflage object segmentation based on feature extraction and mixed attention", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127120T (25 May 2023); https://doi.org/10.1117/12.2679267

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KEYWORDS

Camouflage

Image segmentation

Feature extraction

Data modeling

Visual process modeling

Transformers

Semantics

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