# ResAttUNet: Detecting Marine Debris using an Attention activated Residual UNet Source: [ResAttUNet: Detecting Marine Debris using an Attention activated Residual UNet]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:40:43.923Z Authors: Azhan Mohammed Published: 2022-10-16T10:59:32.000Z ## Abstract Currently, a significant amount of research has been done in field of Remote Sensing with the use of deep learning techniques. The introduction of Marine Debris Archive (MARIDA), an open-source dataset with benchmark results, for marine debris detection opened new pathways to use deep learning techniques for the task of debris detection and segmentation. This paper introduces a novel attention based segmentation technique that outperforms the existing state-of-the-art results introduced with MARIDA. The paper presents a novel spatial aware encoder and decoder architecture to maintain the contextual information and structure of sparse ground truth patches present in the images. The attained results are expected to pave the path for further research involving deep learning using remote sensing images. The code is available at https://github.com/sheikhazhanmohammed/SADMA.git ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on MARIDA](): Semantic segmentation; MARIDA; Macro Precision: 81. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()