DiCAF: A Dual-Input Co-Attention Fusion Network with NMS Ensemble for Underwater Debris Detection

Underwater debris poses a significant threat to marine ecosystems, fisheries, and the tourism industry, necessitating the development of automated vision-based detection systems. Although recent studies have sought to enhance detection performance through underwater image enhancement, improvements in visual quality do not necessarily translate into higher detection accuracy and may, in some cases, degrade performance. To address this discrepancy between perceptual quality and detection reliability, we propose DiCAF, a dual-input co-attention fusion network built upon the latest You Only Look Once v11 detector. The proposed architecture processes both original and enhanced images in parallel and fuses their complementary features through a co-attention module, thereby improving detection stability and consistency. To mitigate high-frequency noise amplified during the enhancement process, a lightweight Gaussian filter is applied as a post-processing step, enhancing robustness against speckle noise commonly introduced by suspended particles in underwater environments. Furthermore, DiCAF incorporates a non-maximum suppression (NMS)-based ensemble that integrates detection outputs from three branches—original, enhanced, and fused—enabling complementary detection of objects missed by individual models and maximizing overall detection performance. Experimental results demonstrate that the proposed single-model DiCAF with Gaussian post-processing achieves an [email protected] of 0.87 and an [email protected]:0.95 of 0.71 on a marine trash dataset. With the NMS-based ensemble, performance improves to 0.91 and 0.75, respectively. Under artificially injected speckle noise conditions, the proposed method maintains superior robustness, achieving an [email protected] of 0.62 and consistently outperforming conventional enhancement-based models.