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Accurate detection and segmentation of marine debris is important for keepingthe water bodies clean. This paper presents a novel dataset for marine debrissegmentation collected using a Forward Looking Sonar (FLS). The datasetconsists of 1868 FLS images captured using ARIS Explorer 3000 sensor. Theobjects used to produce this dataset contain typical house-hold marine debrisand distractor marine objects (tires, hooks, valves,etc), divided in 11 classesplus a background class. Performance of state of the art semantic segmentationarchitectures with a variety of encoders have been analyzed on this dataset andpresented as baseline results. Since the images are grayscale, no pretrainedweights have been used. Comparisons are made using Intersection over Union(IoU). The best performing model is Unet with ResNet34 backbone at 0.7481 mIoU.The dataset is available athttps://github.com/mvaldenegro/marine-debris-fls-datasets/

The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation