A distributed pipeline for DIDSON data processing

Li, Liling; Danner, Tyler; Eickholt, Jesse; McCann, Erin; Pangle, Kevin L.; Johnson, Nicholas S.

doi:10.1109/bigdata.2017.8258458

Cited by 5 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In some complex scenarios, the foreground objects also contain non‐fish objects. Some studies extract features from the detected fish‐like objects and use classifiers such as support vector machine, k‐nearest neighbours, random forest, etc., to distinguish fishes from other objects (Dos Santos et al, 2017; Li et al, 2017).…”

Section: Sonar Data Processing For Fish Detection Tracking and Countingmentioning

confidence: 99%

Monitoring fish using imaging sonar: Capacity, challenges and future perspective

Wei

Duan

2022

Fish and Fisheries

View full text Add to dashboard Cite

The demand for fish products, which provide crucial protein for humans, is rising as the global population grows. In contrast, fish stock is declining due to human activity, environmental changes and overfishing. Fish monitoring provides valuable support data for effective fishery management and ecosystem conservation. The common monitoring methods are based on manual sampling, which is time-consuming, laborious and intrusive. Imaging sonar is a hydroacoustic system that produces acoustic images similar to optical images by transmitting and receiving sound waves, allowing for in situ monitoring of fish non-intrusively in the dark and turbid water environments where optical cameras are limited. In the last decade, imaging sonar, especially high frequency multibeam forward-looking sonar and side-scan sonar, has been widely used in fish monitoring. We reviewed the literature from the previous decade on the use of these two types of imaging sonar in fish species identification, abundance estimation, length measurement and behaviour analysis, as well as the sonar imagery processing concerning fish. The review results show that these imaging sonars are efficient and effective tools for fish monitoring in complex environments. The challenges include(1) the recognition of small fish forming dense aggregations; (2) species identification, which limits their use in species-specific studies; (3) time-consuming massive data processing. Therefore, advanced algorithms for sonar imagery processing and integrations with other sampling technologies are needed for future development.

show abstract

Section: Sonar Data Processing For Fish Detection Tracking and Countingmentioning

confidence: 99%

Monitoring fish using imaging sonar: Capacity, challenges and future perspective

Wei

Duan

2022

Fish and Fisheries

View full text Add to dashboard Cite

show abstract

“…DIDSON generates a significant amount of data and efficient processing of these data has posed challenges when used to assess fish abundance and behavior. As a result, manual processing of the data with traditional tools is often impractical and indeed, the development of algorithms for semi-automated processing of DIDSON data has made it easier to process these large amounts of data 3 , 4 , 8 , 9 . Nevertheless, fully automated fish identification is very challenging because many fish have similar body shapes and sizes and are difficult to distinguish in a DIDSON image.…”

Section: Background and Summarymentioning

confidence: 99%

An underwater observation dataset for fish classification and fishery assessment

McCann

Pangle

et al. 2018

Sci Data

Self Cite

View full text Add to dashboard Cite

Using Dual-Frequency Identification Sonar (DIDSON), fishery acoustic observation data was collected from the Ocqueoc River, a tributary of Lake Huron in northern Michigan, USA. Data were collected March through July 2013 and 2016 and included the identification, via technology or expert analysis, of eight fish species as they passed through the DIDSON’s field of view. A set of short DIDSON clips containing identified fish was curated. Additionally, two other datasets were created that include visualizations of the acoustic data and longer DIDSON clips. These datasets could complement future research characterizing the abundance and behavior of valued fishes such as walleye (Sander vitreus) or white sucker (Catostomus commersonii) or invasive fishes such as sea lamprey (Petromyzon marinus) or European carp (Cyprinus carpio). Given the abundance of DIDSON data and the fact that a portion of it is labeled, these data could aid in the creation of machine learning tools from DIDSON data, particularly for invasive sea lamprey which are amply represented and a destructive invader of the Laurentian Great Lakes.

show abstract

“…Models trained on large general object datasets such as ImageNet (Russakovsky et al, 2015) for general classification tasks can be leveraged through transfer learning, the process of providing smaller amounts of domain specific labeled training data, to reduce the amount of data that must be captured and manually annotated by experts (Ali-Gombe et al, 2017). Largescale distributed computing resources and reusable analysis pipelines are important for training deep learning models (e.g., Li et al, 2017). Fortunately, the resulting trained models can often be run in real-time on smaller embedded devices for continuous monitoring.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning

Kandimalla

Richard²,

Smith³

et al. 2022

Front. Mar. Sci.

View full text Add to dashboard Cite

The Ocean Aware project, led by Innovasea and funded through Canada's Ocean Supercluster, is developing a fish passage observation platform to monitor fish without the use of traditional tags. This will provide an alternative to standard tracking technology, such as acoustic telemetry fish tracking, which are often not appropriate for tracking at-risk fish species protected by legislation. Rather, the observation platform uses a combination of sensors including acoustic devices, visual and active sonar, and optical cameras. This will enable more in-depth scientific research and better support regulatory monitoring of at-risk fish species in fish passages or marine energy sites. Analysis of this data will require a robust and accurate method to automatically detect fish, count fish, and classify them by species in real-time using both sonar and optical cameras. To meet this need, we developed and tested an automated real-time deep learning framework combining state of the art convolutional neural networks and Kalman filters. First, we showed that an adaptation of the widely used YOLO machine learning model can accurately detect and classify eight species of fish from a public high resolution DIDSON imaging sonar dataset captured from the Ocqueoc River in Michigan, USA. Although there has been extensive research in the literature identifying particular fish such as eel vs. non-eel and seal vs. fish, to our knowledge this is the first successful application of deep learning for classifying multiple fish species with high resolution imaging sonar. Second, we integrated the Norfair object tracking framework to track and count fish using a public video dataset captured by optical cameras from the Wells Dam fish ladder on the Columbia River in Washington State, USA. Our results demonstrate that deep learning models can indeed be used to detect, classify species, and track fish using both high resolution imaging sonar and underwater video from a fish ladder. This work is a first step toward developing a fully implemented system which can accurately detect, classify and generate insights about fish in a wide variety of fish passage environments and conditions with data collected from multiple types of sensors.

show abstract

A distributed pipeline for DIDSON data processing

Cited by 5 publications

References 22 publications

Monitoring fish using imaging sonar: Capacity, challenges and future perspective

Monitoring fish using imaging sonar: Capacity, challenges and future perspective

An underwater observation dataset for fish classification and fishery assessment

Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning

Contact Info

Product

Resources

About