Multi-scale feature extraction and TrasMLP encoder module for ocean HABs segmentation

Abstract

Due to tiny edge and texture details of harmful algae blooms(HABs), existing segmentation networks are not effective for HABs segmentation. In order to solve the above problems, this paper proposes a Multi-scale Feature extraction and TrasMLP Encoder Fusion-based network (MFTS). To tackle the complex morphological characteristics and the complex backgrounds of HABs, a TrasMlp module which can effectively identify long-range patterns and adapt network parameters is introduced, enabling accurately parsing of complex algae images. Secondly, the deep convolution module is constructed by combining deep separable convolution with a two-channel attention mechanism to separate the target region from the background. In addition, this paper proposes a Weighted Feature Fusion of Deep Convolution and INRS Encoder Module classification network(FDIR) is proposed to quantify the performance of the image segmentation network. The segmentation results on HABs dataset from AICO Lab show that our proposed MFTS model achieves a miou of 90.02%, outperforming the performance of classical segmentation networks such as U-Net and Mask R-CNN. Compared to original HABs dataset, the segmented result shows a 5.1% improvement in the classification accuracy of the FDIR model.