Developing an orientation and cutting point determination algorithm for a trout fish processing system using machine vision

Abstract

Fish processing in small and medium fish supplying centers requires an intelligent system to operate on different sizes. Therefore, an image processing algorithm was developed to extract the proper head and belly cutting points according to the trout dimensions. The algorithm detects the fish orientation and location of pectoral, anal, pelvic, and caudal fins. In this study, each of the trout images was divided into slices along its length in order to segment the fins and extract cutting points. The channel ‘B’ of RGB color space was considered in both initial segmentation and fin detection stages among the examined channels of RGB, HSV, and L*a*b* color spaces. The back-belly and head-tail sides were detected with an accuracy of 100% based on gray intensity values and head to tail ratio, respectively. Furthermore, performing an analysis of variance (ANOVA) resulted in an F-value of 64.82 among the fins. Conducting a t-test among the mean intensity values of the fins and non-fin regions of channel ‘B’ resulted in the highest distinction with t-values of 90.30, 78.07, 74.28, and 86.01 with p < 0.01 for the pectoral, pelvic, anal, and caudal fins paired with the corresponding non-fin region, respectively. The results showed that the selected ‘B’ channel is the adequate one for fin segmentation. The fin detection process showed an overall sensitivity, specificity, and accuracy of 86.05%, 99.97%, and 99.87%, respectively. By solving the line determination error in 8.24% and the extra object error in 4.12% of the samples, the overall fin identification accuracy was 100%. Finally, after extracting the fin regions, the start point of the pectoral fin and the end point of the anal fin will be applied in the trout processing system as the head and belly cutting points, respectively.