Kresimir Williams , Nathan Lauffenburger , Meng-Che Chuang , Jenq-Neng Hwang , Rick Towler
{"title":"Automated measurements of fish within a trawl using stereo images from a Camera-Trawl device (CamTrawl)","authors":"Kresimir Williams , Nathan Lauffenburger , Meng-Che Chuang , Jenq-Neng Hwang , Rick Towler","doi":"10.1016/j.mio.2016.09.008","DOIUrl":null,"url":null,"abstract":"<div><p><span>We present a method to automatically measure fish from images taken using a stereo-camera system installed in a large trawl (CamTrawl). Different visibility and fish density conditions were evaluated to establish accuracy and precision of image-based length estimates when compared with physical length measurements. The automated image-based length estimates compared well with the trawl catch values and were comparable with manual image processing in good visibility conditions. Greatest agreement with trawl catch occurred when fish were within </span><span><math><mn>2</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>∘</mo></mrow></msup></math></span><span> of fully lateral presentation to the cameras, and within 150 cm of the cameras. High turbidity<span> caused substantial over- and underestimates of length composition, and a greater number of incompletely extracted fish outlines. Multiple estimates of individual fish lengths showed a mean coefficient of variation (CV) of 3% in good visibility conditions. The agreement between manual and automated fish measurement estimates was not correlated with fish length or range from the camera (</span></span><span><math><msup><mrow><mstyle><mi>r</mi></mstyle></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mtext>–</mtext><mn>0.08</mn></math></span>). Implementation of these methods can result in a large increase in survey efficiency, given the effort required to process the trawl catch.</p></div>","PeriodicalId":100922,"journal":{"name":"Methods in Oceanography","volume":"17 ","pages":"Pages 138-152"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.mio.2016.09.008","citationCount":"37","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Methods in Oceanography","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211122016300251","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 37
Abstract
We present a method to automatically measure fish from images taken using a stereo-camera system installed in a large trawl (CamTrawl). Different visibility and fish density conditions were evaluated to establish accuracy and precision of image-based length estimates when compared with physical length measurements. The automated image-based length estimates compared well with the trawl catch values and were comparable with manual image processing in good visibility conditions. Greatest agreement with trawl catch occurred when fish were within of fully lateral presentation to the cameras, and within 150 cm of the cameras. High turbidity caused substantial over- and underestimates of length composition, and a greater number of incompletely extracted fish outlines. Multiple estimates of individual fish lengths showed a mean coefficient of variation (CV) of 3% in good visibility conditions. The agreement between manual and automated fish measurement estimates was not correlated with fish length or range from the camera (). Implementation of these methods can result in a large increase in survey efficiency, given the effort required to process the trawl catch.