Posture-invariant hybrid scaling weight measurement algorithm for live eels

Abstract

To obtain higher economic benefits, large eel breeding companies classify live eels by weight. Due to their strong mobility and smooth body surface, living eels are not suitable for traditional mechanical weight measurement. In this study, a live eel sorting machine based on machine vision was developed, and a novel method was developed for obtaining live eel weight measurements through images. First, a backlit workbench was designed to capture static images of eels, and then the projection area and skeleton length of the images were obtained by image preprocessing. For the eel's body shape, which is generally cylindrical and gradually transitions to a flat tail, the tail posture changes affect the shape of the images; thus, a weight measurement model combining the projected area and the skeleton length was proposed. The optimal scale division coefficient of the weight model was found to be 0.745 by experimentation. Then, select eels of different weight ranges were used for model error verification and to obtain the correction function of the error. The weight gradient was used to confirm the corrected eel weight model. Finally, the system calculation results were compared with the actual measurement results. The root mean square error (RMSE) was 12.94 g, and the mean absolute percentage error (MAPE) was 2.12%. The results show that the proposed method provided a convenient, fast, and low-cost non-contact weight measurement method for live eels, reduced the damage rate of live eels, and can meet the technical requirements of actual production.