# Restoring Ambiguous Boundaries: An Efficient and Robust Framework for Underwater Camouflaged Object Detection

## TL;DR

This paper introduces CAR-YOLO, a new framework for detecting camouflaged objects underwater by resolving boundary ambiguity and improving detection accuracy and efficiency.

## Contribution

Proposes CAR-YOLO, a lightweight detector with novel modules for boundary restoration and uncertainty calibration in underwater object detection.

## Key findings

- CAR-YOLO achieves 27.1% mAP50–95 on UCOD10K, outperforming state-of-the-art methods.
- The model improves performance by 7.7 points on UWB-COT220 compared to YOLOv11.
- CAR-YOLO demonstrates strong generalization in cross-domain experiments on UODD.

## Abstract

The efficacy of Underwater Camouflaged Object Detection (UCOD) is fundamentally constrained by severe boundary ambiguity, where biological mimicry blends targets into complex backgrounds and aquatic optical degradation erodes edge details. We propose a lightweight boundary perception detector named CAR-YOLO (Camouflage Ambiguity Resolution YOLO). Specifically, a frequency-domain dual-path mechanism (FRM-DWT/EG-IWT) leverages selective wavelet aggregation and dynamic injection to recover high-frequency edges. Subsequently, these high-frequency cues are synergized with low-frequency semantic information via the Low-level Adaptive Fusion (LAF) module. To further address noisy samples, an Uncertainty Calibration Head (UCH) refines supervision via prediction consistency. Finally, we constructed specialized datasets based on public data for training and evaluation, including UCOD10K and UWB-COT220. On UCOD10K, CAR-YOLO achieves 27.1% mAP50–95, surpassing several state-of-the-art (SOTA) methods while reducing parameters from 2.58 M to 2.43 M and GFLOPs from 6.3 to 5.9. On the challenging UWB-COT220 benchmark, the model attains 30.7% mAP50–95, marking a 7.7-point improvement over YOLOv11. Furthermore, cross-domain experiments on UODD demonstrate strong generalization. These results indicate that CAR-YOLO effectively mitigates boundary ambiguity, achieving an optimal balance between accuracy, robustness, and efficiency.