Semantic segmentation based on enhanced gated pyramid network with lightweight attention module

Abstract

Semantic segmentation has made tremendous progress in recent years. The development of large datasets and the regression of convolutional models have enabled effective training of very large semantic model. Nevertheless, higher capacity indicates a higher computational problem, thus preventing real-time operation. Yet, due to the limited annotations, the models may have relied heavily on the available contexts in the training data, resulting in poor generalization to previously unseen scenes. Therefore, to resolve these issues, Enhanced Gated Pyramid network (GPNet) with Lightweight Attention Module (LAM) is proposed in this paper. GPNet is used for semantic feature extraction and GPNet is enhanced by the pre-trained dilated DetNet and Dense Connection Block (DCB). LAM approach is applied to habitually rescale the different feature channels weights. LAM module can increase the accuracy and effectiveness of the proposed methodology. The performance of proposed method is validated using Google Colab environment with different datasets such as Cityscapes, CamVid and ADE20K. The experimental results are compared with various methods like GPNet-ResNet-101 and GPNet-ResNet-50 in terms of IoU, precision, accuracy, F1 score and recall. From the overall analysis cityscapes dataset achieves 94.82% pixel accuracy.