# Laplace-guided fusion network for camouflage object detection

## TL;DR

This paper introduces a new network for detecting camouflaged objects by combining frequency domain information with attention mechanisms to better capture object boundaries.

## Contribution

The novel SeCoCR network uses Laplace transforms and a multi-scale attention mechanism to improve camouflage object detection.

## Key findings

- The proposed network outperforms existing frequency-domain methods on COD benchmark datasets.
- The Self-Relation Attention module effectively preserves both semantic and boundary information.
- Low-High Mix Fusion enhances the integration of low- and high-frequency data for better detection.

## Abstract

Camouflaged object detection (COD) aims to identify objects that are visually indistinguishable from their surrounding background, making it challenging to precisely distinguish the boundaries between objects and backgrounds in camouflaged environments. In recent years, numerous studies have leveraged frequency-domain methods to aid in camouflage target detection by utilizing frequency-domain information. However, current methods based on the frequency domain cannot effectively capture the boundary information between disguised objects and the background. To address this limitation, we propose a Laplace transform-guided camouflage object detection network called the Self-Correlation Cross Relation Network (SeCoCR). In this framework, the Laplace-transformed camouflage target is treated as high-frequency information, while the original image serves as low-frequency information. These are then separately input into our proposed Self-Relation Attention module to extract both local and global features. Within the Self-Relation Attention module, key semantic information is retained in the low-frequency data, and crucial boundary information is preserved in the high-frequency data. Furthermore, we design a multi-scale attention mechanism for low- and high-frequency information, Low-High Mix Fusion, to effectively integrate essential information from both frequencies for camouflage object detection. Comprehensive experiments on three COD benchmark datasets demonstrate that our approach significantly surpasses existing state-of-the-art frequency-domain-assisted methods.