The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries

• URL: http://arxiv.org/abs/2106.09178v1
• Date: Wed, 16 Jun 2021 23:53:18 GMT
• Title: The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries
• Authors: Justin Kay and Matt Merrifield
• Abstract summary: We present the Fishnet Open Images Database, a large dataset of fish detection and fine-grained categorization onboard commercial fishing vessels. The dataset consists of 86,029 images containing 34 object classes, making it the largest and most diverse public dataset of fisheries EM imagery to-date. We evaluate the performance of existing detection and classification algorithms and demonstrate that the dataset can serve as a challenging benchmark for development of computer vision algorithms in fisheries.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Camera-based electronic monitoring (EM) systems are increasingly being deployed onboard commercial fishing vessels to collect essential data for fisheries management and regulation. These systems generate large quantities of video data which must be reviewed on land by human experts. Computer vision can assist this process by automatically detecting and classifying fish species, however the lack of existing public data in this domain has hindered progress. To address this, we present the Fishnet Open Images Database, a large dataset of EM imagery for fish detection and fine-grained categorization onboard commercial fishing vessels. The dataset consists of 86,029 images containing 34 object classes, making it the largest and most diverse public dataset of fisheries EM imagery to-date. It includes many of the characteristic challenges of EM data: visual similarity between species, skewed class distributions, harsh weather conditions, and chaotic crew activity. We evaluate the performance of existing detection and classification algorithms and demonstrate that the dataset can serve as a challenging benchmark for development of computer vision algorithms in fisheries. The dataset is available at https://www.fishnet.ai/.

Related papers

• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• FishNet: Deep Neural Networks for Low-Cost Fish Stock Estimation [0.0]
  FishNet is an automated computer vision system for both taxonomic classification and fish size estimation. We use a dataset of 300,000 hand-labeled images containing 1.2M fish of 163 different species. FishNet achieves a 92% intersection over union on the fish segmentation task, a 89% top-1 classification accuracy on single fish species classification, and a 2.3cm mean absolute error on the fish length estimation task.
  arXiv  Detail & Related papers  (2024-03-16T12:44:08Z)
• xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery [52.67592123500567]
  Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. It is now possible to automate detection of dark vessels day or night, under all-weather conditions. xView3-SAR consists of nearly 1,000 analysis-ready SAR images from the Sentinel-1 mission.
  arXiv  Detail & Related papers  (2022-06-02T06:53:45Z)
• A Survey of Fish Tracking Techniques Based on Computer Vision [11.994865945394139]
  This paper presents a review of the advancements of fish tracking technologies over the past seven years-2023. It explores diverse fish tracking techniques with an emphasis on fundamental localization and tracking methods. It also summarizes open-source datasets, evaluation metrics, challenges, and applications in fish tracking research.
  arXiv  Detail & Related papers  (2021-10-06T07:46:35Z)
• Unsupervised Domain Adaption of Object Detectors: A Survey [87.08473838767235]
  Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications. Learning highly accurate models relies on the availability of datasets with a large number of annotated images. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images.
  arXiv  Detail & Related papers  (2021-05-27T23:34:06Z)
• Video-based Hierarchical Species Classification for Longline Fishing Monitoring [17.031967273526803]
  Hierarchical classification based on videos allows for inexpensive and efficient fish species identification of catches from longline fishing. With a known non-overlapping hierarchical data structure provided by fisheries scientists, our method enforces the hierarchical data structure. Our experiments show that the proposed method outperforms the classic flat classification system significantly.
  arXiv  Detail & Related papers  (2021-02-06T06:10:52Z)
• Movement Tracks for the Automatic Detection of Fish Behavior in Videos [63.85815474157357]
  We offer a dataset of sablefish (Anoplopoma fimbria) startle behaviors in underwater videos, and investigate the use of deep learning (DL) methods for behavior detection on it. Our proposed detection system identifies fish instances using DL-based frameworks, determines trajectory tracks, derives novel behavior-specific features, and employs Long Short-Term Memory (LSTM) networks to identify startle behavior in sablefish.
  arXiv  Detail & Related papers  (2020-11-28T05:51:19Z)
• A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis [2.6476746128312194]
  We present DeepFish as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 greenhabitats in the marine-environments of tropical Australia. Our experiments provide an in-depth analysis of the dataset characteristics, and the performance evaluation of several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-08-28T12:20:59Z)
• Temperate Fish Detection and Classification: a Deep Learning based Approach [6.282069822653608]
  We propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. In the second step, we adopt a Convolutional Neural Network (CNN) with the Squeeze-and-Excitation (SE) architecture for classifying each fish in the image without pre-filtering.
  arXiv  Detail & Related papers  (2020-05-14T12:40:57Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)
• GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping [49.777649953381676]
  We contribute a large-scale grasp pose detection dataset with a unified evaluation system. Our dataset contains 87,040 RGBD images with over 370 million grasp poses.
  arXiv  Detail & Related papers  (2019-12-31T18:15:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.