Accurate Segmentation of Tilapia Fish Body Parts Based on Deeplabv3+ for Advancing Phenotyping Applications

Abstract

As an important economic fish resource, germplasm resources and phenotypic measurements of tilapia are of great importance in the direction of culture and genetic improvement. Furthermore, accurate identification and precise localization of tilapia body parts are crucial for enabling key technologies such as automated capture and precise cutting. However, there are some problems in the semantic segmentation of tilapia fish, including the accuracy of target edge segmentation and the ambiguity in segmenting small targets. To improve the accuracy of semantic segmentation of tilapia parts in real farming environments, an improved Deeplabv3+ network model method is proposed for implementing tilapia part segmentation to facilitate phenotypic measurements on tilapia in this paper. The CBAM module is embedded in the encoder, which can improve the accurate identification and localization of tilapia parts by adaptively adjusting the channel weights and spatial weights and better focus on the key features and spatial connections of tilapia body parts. Furthermore, the decoding part of the Deeplabv3+ model is optimized by using SENet, which greatly increases the segmentation accuracy of the network by establishing the interdependence between channels while suppressing useless features. Finally, model performance is tested and compared with the original network and other methods on the tilapia part segmentation dataset. The experimental results show that the segmentation performance of the improved network is better than other networks, such as PSPNet and U-Net, and the IoU values in the head, fins, trunk, and tail of the fish body are 9.78, 2.27, 6.27, and 6.58 percentage points higher than those of the Deeplabv3+ network, respectively. The results validate the effectiveness of our approach in solving the above problems encountered in the semantic segmentation of tilapia parts.