A Method for Estimating the Injection Position of Turbot (<i>Scophthalmus maximus</i>) Using Semantic Segmentation

Abstract

Fish vaccination plays a vital role in the prevention of fish diseases. Inappropriate injection positions will cause a low immunization rate and even death. Currently, traditional visual algorithms have poor robustness and low accuracy due to the specificity of the placement of turbot fins in the application of automatic vaccination machines. To address this problem, we propose a new method for estimating the injection position of the turbot based on semantic segmentation. Many semantic segmentation networks were used to extract the background, fish body, pectoral fin, and caudal fin. In the subsequent step, the segmentations obtained from the best network were used for calculating body length (BL) and body width (BW). These parameters were employed for estimating the injection position. The proposed Atten-Deeplabv3+ achieved the best segmentation results for intersection over union (IoU) on the test set, with 99.3, 96.5, 85.8, and 91.7 percent for background, fish body, pectoral fin, and caudal fin, respectively. On this basis, the estimation error of the injection position was 0.2 mm–4.4 mm, which is almost within the allowable injection area. In conclusion, the devised method was able to correctly differentiate the fish body from the background and fins, meaning that the extracted area could be successfully used for the estimation of injection position.

Keywords

• turbot
• vaccination
• deeplabv3+
• attention mechanism
• measurement
• aquaculture