Automatic Detection of Features from Atlantic Salmon by Classical Image Processing

• Main Authors: Aanestad Lende, Christer, Lundal, Joachim Nising
• Other Authors: Austvoll, Ivar
• Format: Master Thesis
• Language: English
• Published: University of Stavanger, Norway 2019
• Subjects: informasjonsteknologi; akvakultur; industriell økonomi; VDP::Social science: 200::Economics: 210; VDP::Technology: 500::Information and communication technology: 550; Atlantic salmon
• Online Access: http://hdl.handle.net/11250/2628076

Master's thesis in Cybernetics and signal Processing / Industrial economics The methods created for this project aim to locate the salmon in the image and extract features from it. The aim of the features are to recognize individual salmon from each other. Individual identification, done by RFID today, is important in the Norwegian aquaculture industry, mostly for scientific purposes. If this could be implemented by machine vision instead, it could be expanded to commercial purposes and tracking of larger masses, which could be economically beneficial for the industry. Based on reasonable assumptions, some economic scenarios were computed to estimate potential savings that could be achieved by successful implementation of such a system. Based on the assumptions, it is reasonable to believe that this could save roughly 5,04 MNOK every year per offshore fish cage applied to. The potential costs are however somewhat uncertain. K-means clustering was used to extract the salmon from the image. This was successful for all images. It should be noted that the data-set was cleaned of images which did not meet certain requirements. A method was developed to detect the nose and tail tips of the salmon mainly to estimate its orientation. It worked on all images largely due to the successful cropping done by the k-means clustering. Another method was created to detect the pectoral fin on the salmon, using segmentation by thresholding, as well as structural measures and area-thresholds. It achieved a best success rate at 99.2% on 537 images from one data-set(main set) and at worst a success rate of 93.5% on 246 images from another data-set(second set). It was important to detect the gill-opening on the salmon, which would lead to extract a ROI around the head. The method for locating the gill-opening therefore had an important task in detecting the back of the gills, towards the body, such that the area of the head was not cropped too small. The method had an average of detecting 5.32 pixels away from the gill-opening towards the ...