Automatic single fish detection with a commercial echosounder using YOLO v5 and its application for echosounder calibration

Abstract

Nowadays, most fishing vessels are equipped with high-resolution commercial echo sounders. However, many instruments cannot be calibrated and missing data occur frequently. These problems impede the collection of acoustic data by commercial fishing vessels, which are necessary for species classification and stock assessment. In this study, an automatic detection and classification model for echo traces of the Pacific saury (Cololabis saira) was trained based on the algorithm YOLO v5m. The in situ measurement value of the Pacific saury was measured using single fish echo trace. Rapid calibration of the commercial echo sounder was achieved based on the living fish calibration method. According to the results, the maximum precision, recall, and average precision values of the trained model were 0.79, 0.68, and 0.71, respectively. The maximum F1 score of the model was 0.66 at a confidence level of 0.454. The living fish calibration offset values obtained at two sites in the field were 116.30 dB and 118.19 dB. The sphere calibration offset value obtained in the laboratory using the standard sphere method was 117.65 dB. The differences between in situ and laboratory calibrations were 1.35 dB and 0.54 dB, both of which were within the normal range.