DFFNet: An IoT-perceptive dual feature fusion network for general real-time semantic segmentation

Semantic segmentation is a valuable yet challenging research in the Internet of Things (IoT), especially for some real-time and resource-constrained applications. Recently we witness a strong tendency of fusing multi-level features or multi-scale context information to achieve promising segmentation performance. However, we find that existing literature has at least one of the following issues: 1) relying on more resource-consuming feature extraction operations, e.g., standard convolution with large kernels, for multiple information fusion; 2) seldom considering that how to narrow the semantic gap between multi layer features, leading to sub-optimal performance. To tackle these issues, we propose a novel IoT-perceptive Dual Feature Fusion Network (DFFNet) for semantic segmentation, which aims to leverage multi-level features and multi-scale context information in an efficient yet effective manner. Specifically, the multi-level feature fusion module (MFFM), which enhances the semantic consistency between multi-level features by two attention refinement blocks, is proposed to exploit multi-layer features for jointly learning spatial and semantic information with small overheads. Moreover, a multi-scale component termed as lightweight semantic pyramid module (LSPM) is presented to improve the efficiency of context encoding by depthwise factorized convolution operations. Extensive experimental results on benchmarks have demonstrated that DFFNet achieves better performance than existing advanced methods. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

Semantic segmentation research in IoT has been challenging, with recent trends focused on fusing multi-level features and multi-scale context information. However, existing literature faces issues of resource-consuming feature extraction and semantic gap between multi-layer features. To address these challenges, a novel IoT-perceptive Dual Feature Fusion Network (DFFNet) is proposed for effective utilization of features and context information.