Semantic simultaneous localization and mapping augmented with deep learning provides an effective solution for handling dynamic scenes. However, this technology still faces challenges of high computational resource consumption and model complexity. To address these issues, this paper proposes a lightweight semantic segmentation network based on improvements to BlendMask. Firstly, a lightweight Ghost-depthwise separable convolution with efficient channel attention block (GDS-ECA) module is designed. This module replaces a few convolution operations in Ghost convolution with depthwise separable convolution to reduce parameters and computational load, while incorporating an attention mechanism to enhance feature representation capabilities. Secondly, a bottleneck GDS-ECA attention transformer network (BGTNet) is proposed, which applies GDS-ECA convolution to the neck module’s convolution layers to improve feature extraction precision. Additionally, traditional convolutions in the feature pyramid network (FPN) are replaced with GDS-ECA convolutions, creating a lightweight FPN (L-FPN). Combined with BGTNet, this forms the Backbone of the proposed semantic segmentation network. Finally, experiments on the COCO dataset validate the improvements, demonstrating a 7.3 ms reduction in processing time per image, and a 1.5% improvement in average precision.
LIAN Xiaofeng, KANG Maomao, TAN Li, WANG Yanli
. A Semantic Segmentation Network Based on Lightweight Convolutional Modules[J]. Journal of Applied Sciences, 2025
, 43(1)
: 66
-79
.
DOI: 10.3969/j.issn.0255-8297.2025.01.005
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