# Semantic Segmentation of Sika Deer Antler Image by U-Net Based on Two-Dimensional Discrete Wavelet Transform Fusion and Multi-Attention Mechanism

## TL;DR

This paper introduces a new U-Net-based model for accurately segmenting sika deer antler images in complex environments, improving segmentation accuracy and efficiency.

## Contribution

The novel SDAS-Net model integrates 2D-DWT fusion and multi-attention mechanisms for improved antler segmentation in challenging conditions.

## Key findings

- The SDAS-Net model achieved 92.12% MIoU and 93.63% PA index on the sika deer antler dataset.
- The model maintains high precision while increasing parameters by only 6.9%.
- The model effectively handles complex breeding environment challenges like light changes and occlusions.

## Abstract

Monitoring the antler growth status of sika deer is of great significance for sika deer antler grading and sika deer class identification, and it also has a role in promoting the process of intelligent breeding of sika deer. In this study, a new network model for segmentation of sika deer antlers was developed based on the U-Net by incorporating innovative modules and attention mechanisms. The method was evaluated using datasets of antler images from adult sika deer. It not only has high accuracy in segmentation tasks but is also very friendly to hardware resources. This provides the data and technological support for sika deer antler quality assessment and grading.

At present, the monitoring technology of the growth status of sika deer antlers faces many challenges in a complex breeding environment (such as light change, object occlusion, etc.). More importantly, an effective method for the segmentation of sika deer antlers is still lacking, which hinders the development of subsequent quality classification of sika deer antlers. In order to fill the research gap and lay a foundation for future sika deer antler quality classification, this paper proposed an improved semantic segmentation model based on U-Net, named SDAS-Net. In order to improve the segmentation accuracy and generalization ability of the model in a complex environment, we introduced a two-dimensional discrete wavelet transform module (2D-DWT) in the encoder head to reduce noise interference and enhance the ability to capture features. In order to compensate for the loss of feature information caused by 2D-DWT, we embedded the Star Blocks module in the encoder. In addition, the efficient mixed channel attention (EMCA) module was introduced to adaptively enhance key feature channels in the decoder, and the dual cross-attention mechanism (DCA) module was used to fuse high-dimensional features in skip connections. To verify the validity of the model, we constructed a 1055-image sika deer antler dataset (SDR). The experimental results show that compared with the baseline model, the performance of the SDAS-Net model is significantly improved, reaching 92.12% in MIoU and 93.63% in the PA index, and the number of parameters is only increased by 6.9%. The results show that the SDAS-Net model can effectively deal with the task of sika deer antler segmentation in a complex breeding environment while maintaining high precision.