Subaquatic Garbage Recognition Based on Improved YOLOv11 Network

In the field of marine ecosystem conservation and underwater garbage management, accurately identifying diverse debris is critical to improve cleanup efficiency and advance intelligent underwater operations. However, reflection, occlusion, color attenuation, object deformation in complex underwater environment render the traditional detection models impractical for dynamic scenarios. To address these issues, this paper proposes an underwater debris recognition model based on an enhanced YOLOv11 network. The improvements are as follows:a) FasterNet is adopted as the backbone network, which balances lightweight and high-fidelity feature extraction, effectively optimizing the retention of small-object features.b) SOAH, an occlusion-aware attention mechanism is introduced to reconstruct the detection head. This enables the module to strengthen the response to occluded targets during fusion process, thereby amplifying recognition performance in complex backgrounds.c) DAttention is utilized to replace the conventional spatial attention mechanism, which makes the DUAM module adaptable to partial texture deformation and increase the recognition accuracy of heterogeneous targets. The datasets comprises 9 major categories and 40 subcategories underwater debris images, collected from natural marine environment of varying depths and lighting conditions. It virtually contains typical challenges such as aquatic particle interference and uneven illumination. Experimental results show that the advanced network boosts from 66.9% to 77.3%, a 10.4% increase was observed. This proposed methodology demonstrates high precision and stability in complex conditions, providing robust support for underwater marine debris detection.