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Real-time tracking of recreational boats in coastal areas using deep learning

Ocean and Coastal Management 2025年 267.0卷

作者： Signaroli, Marco Lana, Arancha Cutolo, Eugenio Alós, Josep Gonzalez-Cid, Yolanda Institut Mediterrani d'Estudis Avançats IMEDEA (CSIC-UIB) Marine Research in Ecological and Social Systems Group (MARES) C/. Miquel Marquès 21 Illes Balears Esporles 07190 Spain AtalaIA Detection Technologies Av. Gabriel Alomar I Villalonga 22 Illes Balears Palma 07006 Spain Institut Mediterrani d'Estudis Avançats IMEDEA (CSIC-UIB) DataLab Service C/. Miquel Marquès 21 Illes Balears Esporles 07190 Spain IMT Atlantique Lab-STICC UMR CNRS 6285 and Odyssey Inria/IMT Technopôle Brest-Iroise 655 Avenue du Technopôle Plouzané 29280 France Universitat de les Illes Balears (UIB) Department of Mathematics and Computer Science Systems Robotics and Vision Group (SRV) Cra. de Valldemossa km 7.5 Illes Balears Palma 07122 Spain

To effectively manage and conserve coastal ecosystems, accurate spatial data on marine recreational activities are crucial. This study introduces a deep-learning-based system designed for real-time detection and tracking of recreational vessels in coastal environments using cameras. We fine-tuned and evaluated two object detection and classification algorithms, YOLOv5 and YOLOv7, for automated, real-time vessel detection, classification and positioning within Marine Protected Areas (MPAs). Additionally, we optimized two multiple object tracking algorithms, StrongSORT and ByteTrack, for tracking the movements of the detected vessels in sequential timeframes. We implemented the best combination (YOLOv5 and ByteTrack) on an NVIDIA Jetson platform, an edge computing device specifically designed for AI applications, conducting thorough benchmarking across various simulated hardware configurations to determine its minimal computational and power needs. Then, we conducted field tests by positioning the system on a coastal cliff overlooking a recreational fishery located in a partial MPA. These tests aimed to validate the system's real-time operational viability and to acquire precise vessel trajectories. The results confirmed the system's efficacy and its data collection capabilities within a real marine environment. Finally, we evaluated two camera calibration techniques for converting image trajectories to geographic coordinates: a projective transformation with homography for accurate perspective adjustment, and an innovative neural network-based approach. The system we have developed could markedly enhance the monitoring and surveillance capabilities within MPAs, generating spatial-temporal data of recreational fishing effort that can be easily transferred to other case studies. © 2025 The Authors

关键词： Artificial intelligence Boat detection Deep learning Fishing effort Marine protected areas Multiple object tracking Recreational fishing

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Robust color image segmentation through tensor voting

Robust color image segmentation through tensor voting

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International Conference on Pattern Recognition

作者： Moreno, Rodrigo Garcia, Miguel Angel Puig, Domenec Rovira I Virgili University Intelligent Robotics and Computer Vision Group Dept. of Computer Science and Mathematics Av. Països Catalans 26 43007 Tarragona Spain Autonomous University of Madrid Dept. of Informatics Engineering Francisco Tomas y Valiente 11 28049 Madrid Spain

ISBN: (纸本)9780769541099

This paper presents a new method for robust color image segmentation based on tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. First, an adaptation of tensor voting to both image denoising and robust edge detection is applied. Second, pixels in the filtered image are classified into likely-homogeneous and likely-inhomogeneous by means of the edginess maps generated in the first step. Third, the likely-homogeneous pixels are segmented through an efficient graph-based segmenter. Finally, a modified version of the same graph-based segmenter is applied to the likely-inhomogeneous pixels in order to obtain the final segmentation. Experiments show that the proposed algorithm has a better performance than the state-of-the-art. © 2010 IEEE.

关键词： Pixels

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Detecting abnormal mammographic cases in temporal studies using image registration features

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12th International Workshop on Breast Imaging, IWDM 2014

作者： Martí, Robert Díez, Yago Oliver, Arnau Tortajada, Meritxell Zwiggelaar, Reyer Lladó, Xavier Computer Vision and Robotics Group University of Girona Av. Lluís Santaló 17071 Spain Department of Computer Science Aberystwyth University Ceredigion SY23 3DB United Kingdom

ISBN: (纸本)9783319078861

Image registration is increasingly being used to help radiologists when comparing temporal mammograms for lesion detection and classification. This paper evaluates the use of image and deformation features extracted from image registration results in order to detect abnormal cases with masses. Using a dataset of 264 mammographic images from 66 patients (33 normals and 33 with masses) results show that the use of a non-rigid registration method clearly improves detection results compared to no registration (AUC: 0.76 compared to 0.69). Moreover, feature combination using left and right breasts further improves the performance (AUC to 0.88) compared to single image features. © 2014 Springer International Publishing.

关键词： Image registration

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Gabor-based texture classification through efficient prototype selection via normalized cut

Gabor-based texture classification through efficient prototy...

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IEEE International Conference on Image Processing

作者： Jaime Melendez Domenec Puig Miguel Angel Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Spain Department of Informatics Engineering Autonomous University of Madrid Spain

This paper presents a new efficient technique for supervised pixel-based texture classification. The proposed scheme first performs a selection process that automatically determines a subset of prototypes that characterize each texture class based on the outcome of a multichannel Gabor wavelet filter bank. Then, every image pixel is classified into one of the given texture classes by using a K-NN classifier fed with the prototypes determined previously. The proposed technique is compared to previous texture classifiers by using both Brodatz and real outdoor textured images.

关键词： Prototypes Gabor filters Pixel Feature extraction Filter bank Testing intelligent robots Image texture analysis Pattern recognition Image recognition

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A 3D local energy surface detector for confocal microscope imalges 3

A 3D local energy surface detector for confocal microscope i...

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3rd Australian and New Zealand Conference on intelligent Information Systems, ANZIIS 1995

作者： Pudney, Chris Kovesi, Peter Robbins, Ben Biomedical Confocal Microscopy Research Centre Department of Pharmacology United States Robotics and Vision Research Group Department of Computer Science University of Western Australia NedlandsWA6907 Australia

ISBN: (纸本)0864224303

The ability to detect features within confocal microscope images is important for the interpretation and analysis of such data. Most detectors are gradient-based, and so are sensitive to noise, and fail to accurately locate some feature types that are important in confocal microscopy. The local energy feature detector developed by Morrone and Owens marks locations where there is maximal congruence of phase in the Fourier components of an image. Points of maximal phase congruency occur at all common feature profiles: step and roof edges, line features and Mach bands. A 3D implementation of the local energy feature detector, suitable for confocal microscope data, is presented. The detector computes local energy by convolving an image with oriented pairs of 3D filters. The filters are 3D versions of Morlet wavelets. To increase the speed of the convolution, the filters are designed in frequency-space and multiplied by the image's Fourier transform. Results are presented for real confocal images and synthetic 3D image volumes. © 1995 IEEE. All rights reserved.

关键词： Microscopes

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Reducing the complexity of geometric selective disassembly

Reducing the complexity of geometric selective disassembly

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IEEE International Workshop on intelligent Robots and Systems (IROS)

作者： M.A. Garcia A. Larre B. Lopez A. Oller Department of Computer Science and Mathematics Intelligent Robotics and Computer Vision Group Tarragona Spain Department of Electrical Electronic and Automation Engineering Rovira i Virgili University Tarragona Spain

This paper presents an efficient technique for determining a low-cost disassembly sequence suitable to extract a subset of s components from an assembly containing n components, e of which are exterior (e/spl Lt/n). The most efficient solution to this so-called geometric selective disassembly problem is the wave propagation algorithm, which is reported to have a computational complexity of O(sn/sup 2/). Instead, the complexity of the proposed algorithm is O(enlogn) when s/spl Lt/n, and O(sn) when s/spl sime/n. Experimental results with synthetic 3D assemblies are presented.

关键词： Assembly intelligent robots robotics and automation Computational complexity Recycling Fasteners Polynomials Costs Robot vision systems computer vision

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On Adapting Pixel-based Classification to Unsupervised Texture Segmentation

On Adapting Pixel-based Classification to Unsupervised Textu...

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International Conference on Pattern Recognition

作者： Jaime Melendez Domenec Puig Miguel Angel Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain Department of Informatics Engineering Autonomous University of Madrid Madrid Spain

ISBN: (纸本)9781424475421

An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture patterns present in the images to be segmented. A new efficient methodology for unsupervised image segmentation based on texture is proposed. It takes advantage of a supervised pixel-based texture classifier trained with feature vectors associated with a set of texture patterns initially extracted through a clustering algorithm. Therefore, the final segmentation is achieved by classifying each image pixel into one of the patterns obtained after the previous clustering process. Multi-sized evaluation windows following a top-down approach are applied during pixel classification in order to improve accuracy. The proposed technique has been experimentally validated on MeasTex, VisTex and Brodatz compositions, as well as on complex ground and aerial outdoor images. Comparisons with state-of the-art unsupervised texture segmenters are also provided.

关键词： Image segmentation Pixel Clustering algorithms Accuracy Feature extraction Classification algorithms Image edge detection

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A new methodology for evaluation of edge detectors

A new methodology for evaluation of edge detectors

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IEEE International Conference on Image Processing

作者： Rodrigo Moreno Domenec Puig Carme Julià Miguel Angel Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain Department of Informatics Engineering Autonomous University of Madrid Madrid Spain

This paper defines a new methodology for evaluating edge detectors through measurements on edginess maps instead of on binary edge maps as previous methodologies do. These measurements avoid possible bias introduced by the application-dependent process of generating binary edge maps from edginess maps. The features of completeness, discriminability, precision and robustness, which a general-purpose edge detector must comply with, are introduced. The R, DS, P and FAR-measurements in addition to PSNR applied to the edginess maps are defined to assess the performance of edge detection. The R, DS, P and FAR-measurements can be seen as generalizations of previously proposed measurements on binary edge maps. Well-known and state-of-the-art edge detectors have been compared by means of the new proposed metrics. Results show that it is difficult for an edge detector to comply with all the proposed features.

关键词： Image edge detection Detectors intelligent robots Robustness Robot vision systems computer vision computer science mathematics Informatics Tin

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Robust color edge detection through tensor voting

Robust color edge detection through tensor voting

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IEEE International Conference on Image Processing

作者： Rodrigo Moreno Miguel Angel Garcia Domenec Puig Carme Julià Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain Department of Informatics Engineering Autonomous University of Madrid Madrid Spain

This paper presents a new method for color edge detection based on the tensor voting framework, a robust perceptual grouping technique used to extract salient information from noisy data. The tensor voting framework is adapted to encode color information via tensors in order to propagate them into a neighborhood through a voting process specifically designed for color edge detection by taking into account perceptual color differences, region uniformity and edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by means of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Experiments show that the proposed algorithm is more robust and has a similar performance in precision when compared with the state-of-the-art.

关键词： Robustness Image edge detection Tensile stress Voting Colored noise Color Detectors Eigenvalues and eigenfunctions intelligent robots computer vision

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Robust Color Image Segmentation through Tensor Voting

Robust Color Image Segmentation through Tensor Voting

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International Conference on Pattern Recognition

作者： Rodrigo Moreno Miguel Angel Garcia Domenec Puig Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain Department of Informatics Engineering Autonomous University of Madrid Madrid Spain

ISBN: (纸本)9781424475421

This paper presents a new method for robust color image segmentation based on tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. First, an adaptation of tensor voting to both image denoising and robust edge detection is applied. Second, pixels in the filtered image are classified into likely-homogeneous and likely-inhomogeneous by means of the edginess maps generated in the first step. Third, the likely-homosgeneous pixels are segmented through an efficient graph-based segmenter. Finally, a modified version of the same graph-based segmenter is applied to the likely-inhomogeneous pixels in order to obtain the final segmentation. Experiments show that the proposed algorithm has a better performance than the state-of-the-art.

关键词： Pixel Image segmentation Tensile stress Robustness Image color analysis Image edge detection Noise measurement

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