A lightweight network for smoke semantic segmentation

Feiniu Yuan

doi:10.1016/j.patcog.2022.109289

2023-05-01

PRE

DOI：10.1016/j.patcog.2022.109289

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摘要

En 中文

To obtain real-time performance on computation limited devices, we propose a lightweight network for smoke segmentation. To enhance the ability of feature encoding, we first propose an Attention Encoding Module (AEM) by designing a Channel Split and Shuffle Attention Module (CSSAM), which can extract powerful features and reduce computations simultaneously. CSSAM adopts Channel split and shuffle to greatly reduce learnable parameters for improving computation speed, and uses attention mechanism to focus on salient objects to enhance the effectiveness of features. In addition, AEM repeatedly stacks CSSAM in different encoding stages to achieve scale invariance. For the middle-level features of encoding stages, we propose a Spatial Enhancement Module (SEM) to boost the representation ability of spatial details. SEM concatenates feature maps produced by average and maximum pooling to achieve domi-nant and global responses, which are then weighted by the activated output of global average pooling to generate attention features. In the highest level of encoding stages, we present a Channel Attention Module (CAM) to explicitly model interdependency between channels. By reshaping 2D features into 1D features, we use element-wise matrix multiplications to reduce computation complexity for extracting channel-related information. Finally, we design a Feature Fusion Module (FFM) and a Global Coefficient Path (GCP) to fuse the outputs of SEM and CAM in an attention way for further improving robustness of final features. Experiments show that our method is significantly superior to existing state-of-the-art al-gorithms in smoke datasets, and also obtains excellent results in both synthetic and real smoke datasets. However, our method has less than 1 M network parameters.(c) 2023 Elsevier Ltd. All rights reserved.

Keyword:

Smoke semantic segmentation

Deep learning

Attention mechanism

Lightweight network

Channel split and shuffle