Accurate detection and segmentation of marine debris is important for keeping the water bodies clean. This paper presents a novel dataset for marine debris segmentation collected using a Forward Looking Sonar (FLS). The dataset consists of 1868 FLS images captured using ARIS Explorer 3000 sensor. The objects used to produce this dataset contain typical house-hold marine debris and distractor marine objects (tires, hooks, valves,etc), divided in 11 classes plus a background class. Performance of state of the art semantic segmentation architectures with a variety of encoders have been analyzed on this dataset and presented as baseline results. Since the images are grayscale, no pre-trained weights have been used. Comparisons are made using In...
This paper addresses a sonar image segmentation method employing a Robust A*-Search Image Segmentati...
Machine learning and neural networks are now ubiquitous in sonar perception, but it lags behind the ...
International audienceThis paper presents an automatic sea surface object clustering and tracking in...
Accurate detection and segmentation of marine debris is important for keeping the water bodies clean...
Garbage and waste disposal is one of the biggest challenges currently faced by mankind. Proper waste...
The large amount of debris in our oceans is a global problem that dramatically impacts marine fauna ...
In this article, we present the first large-scale data set for underwater ship lifecycle inspection,...
Underwater mines are a cost-effective method in asymmetric warfare, and are commonly used to block s...
Sidescan and forward looking sonars are some of the most widely used imaging systems for obtaining l...
Autonomous underwater garbage grasping and collection pose a great challenge to underwater robots. T...
Forward Looking Sonars (FLS) are a typical choiceof sonar for autonomous underwater vehicles. They a...
Recognition of marine debris represent a difficult task due to the extreme variability of the marine...
AbstractAutonomous Underwater Vehicles (AUVs) are generally relying on Forward Looking SONAR (FLS) d...
Vision-based semantic segmentation of waterbodies and nearby related objects provides important info...
With the global issue of marine debris ever expanding, it is imperative that the technology industry...
This paper addresses a sonar image segmentation method employing a Robust A*-Search Image Segmentati...
Machine learning and neural networks are now ubiquitous in sonar perception, but it lags behind the ...
International audienceThis paper presents an automatic sea surface object clustering and tracking in...
Accurate detection and segmentation of marine debris is important for keeping the water bodies clean...
Garbage and waste disposal is one of the biggest challenges currently faced by mankind. Proper waste...
The large amount of debris in our oceans is a global problem that dramatically impacts marine fauna ...
In this article, we present the first large-scale data set for underwater ship lifecycle inspection,...
Underwater mines are a cost-effective method in asymmetric warfare, and are commonly used to block s...
Sidescan and forward looking sonars are some of the most widely used imaging systems for obtaining l...
Autonomous underwater garbage grasping and collection pose a great challenge to underwater robots. T...
Forward Looking Sonars (FLS) are a typical choiceof sonar for autonomous underwater vehicles. They a...
Recognition of marine debris represent a difficult task due to the extreme variability of the marine...
AbstractAutonomous Underwater Vehicles (AUVs) are generally relying on Forward Looking SONAR (FLS) d...
Vision-based semantic segmentation of waterbodies and nearby related objects provides important info...
With the global issue of marine debris ever expanding, it is imperative that the technology industry...
This paper addresses a sonar image segmentation method employing a Robust A*-Search Image Segmentati...
Machine learning and neural networks are now ubiquitous in sonar perception, but it lags behind the ...
International audienceThis paper presents an automatic sea surface object clustering and tracking in...