Improving the Deeplabv3+ Model with Attention Mechanisms Applied to Eye Detection and Segmentation

Abstract

Research on eye detection and segmentation is even more important with mask-wearing measures implemented during the COVID-19 pandemic. Thus, it is necessary to build an eye image detection and segmentation dataset (EIMDSD), including labels for detecting and segmenting. In this study, we established a dataset to reduce elaboration for chipping eye images and denoting labels. An improved DeepLabv3+ network architecture (IDLN) was also proposed for applying it to the benchmark segmentation datasets. The IDLN was modified by cascading convolutional block attention modules (CBAM) with MobileNetV2. Experiments were carried out to verify the effectiveness of the EIMDSD dataset in human eye image detection and segmentation with different deep learning models. The result shows that the IDLN model achieves the appropriate segmentation accuracy for both eye images, while the UNet and ISANet models show the best results for the left eye data and the right eye data among the tested models.

Keywords

• eye image dataset
• detection
• segmentation
• deep learning model
• benchmark
• performance