Low-dimensional feature enhanced camouflaged object detection

Xin Wang; Zhao Zhang; Junfeng Xu

doi:10.1117/12.2686401

10 August 2023 Low-dimensional feature enhanced camouflaged object detection

Xin Wang, Zhao Zhang, Junfeng Xu

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127593S (2023) https://doi.org/10.1117/12.2686401
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

Camouflaged Object Detection (COD) is a challenging vision task that aims at locating objects having high similarity in appearance with its background. Existing COD methods are highly dependent on the high-dimensional feature of camouflaged object while neglecting details within the low-dimensional feature. In this paper, we highlight lowdimensional information to enlarge the discrepancies between objects and their surroundings. We propose a CNN-based network ShallowNet with enhanced low-dimensional feature to retain more differences in the process of encoding. The proposed approach shows enhanced effectiveness both qualitatively and quantitatively compared with existing COD methods regarding various datasets.

Citation Download Citation

Xin Wang, Zhao Zhang, and Junfeng Xu "Low-dimensional feature enhanced camouflaged object detection", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127593S (10 August 2023); https://doi.org/10.1117/12.2686401

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KEYWORDS

Object detection

Camouflage

Convolution

Education and training

Visualization

Binary data

Intelligence systems

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