BFANet: Fine-Grained Boundary-Aware Semantic Segmentation Driven by Dynamic Feature Alignment
Wang Zhang1, Lanlan Li2, Jiayi Xing1, Qiangqiang Yao1,*
1 School of Mechanical Engineering, Qinghai University, Xining, China
2 School of Medicine, Qinghai University, Xining, China
* Corresponding Author: Qiangqiang Yao. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.083877
Received 12 April 2026; Accepted 23 June 2026; Published online 31 July 2026
Abstract
Lightweight semantic segmentation remains challenging because compact backbones often weaken feature discriminability and lose fine-grained boundary details. In DeepLabV3+-style encoder-decoder architectures, the direct fusion of high-level semantic features and low-level spatial features may introduce semantic-spatial misalignment, resulting in blurred object contours and fragmented predictions. To address these issues, this paper proposes BFANet, a boundary-aware lightweight semantic segmentation framework based on DeepLabV3+ with a MobileNetV2 backbone. BFANet integrates parameter-free SimAM feature refinement, low-level-guided Dynamic Feature Alignment, and progressive decoder fusion to enhance discriminative feature responses, reduce cross-level feature inconsistency, and recover fine boundary structures. Experiments on a curated PASCAL VOC 2012 subset demonstrate that BFANet achieves consistent improvements over the DeepLabV3+ baseline with only a negligible increase in model complexity. Specifically, BFANet improves mIoU from 77.57% to 80.23%, mPA from 86.36% to 88.05%, and mDice from 87.52% to 88.78%. Moreover, the boundary-level evaluation further shows that BFANet improves Boundary F1 from 57.28% to 61.55% and Boundary IoU from 48.74% to 51.42%, indicating better preservation of fine object contours and more accurate boundary alignment. Meanwhile, the parameter count increases only from 5.82M to 5.84M, and GFLOPs increase from 53.03 to 53.75. These results show that BFANet provides a better balance among segmentation accuracy, boundary quality, and lightweight efficiency, especially for small objects, thin structures, and boundary-sensitive categories.
Keywords
Semantic segmentation; boundary-aware; dynamic feature alignment; lightweight network; fine-grained segmentation; DeepLabV3+
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