Unstructured road segmentation algorithm based on improved BiSeNet
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摘要:
非结构化道路通常没有清晰的边界及车道线，环境较为复杂，传统的基于道路纹理、颜色特征的分割方法无法满足实时性和准确性的要求。针对非结构化道路场景，提出了基于改进BiSeNet的轻量化语义分割模型，采用轻量化主干提取网络和引入深度可分离卷积，优化速度控制；在最后的特征融合阶段引入通道注意力，自适应地选择重要特征，抑制冗余信息，提高非结构化道路分割的准确性。改进后模型参数量仅有1.11×106，检测速度提升18.83%，F1-score达到了96.74%。对比其他主流语义分割模型，该算法具有参数量小、速度快、准确率高等优势，可为非结构化道路场景下无人驾驶车辆的安全运行提供参考。
Abstract:Unstructured roads usually have no clear boundaries and lane lines, and the environment is more complex. The traditional segmentation methods based on road texture and color features cannot meet the requirements of real-time performance and accuracy. For unstructured road scenes, a lightweight semantic segmentation model based on improved BiSeNet was proposed, which adopted the lightweight trunk extraction network and introduced the depthwise separable convolution to optimize the speed control. The channel attention was introduced in the final feature fusion stage to adaptively select important features, suppress redundant information, and improve the accuracy of unstructured road segmentation. The number of parameters of the improved model is only 1.11×106, the detection speed is increased by 18.83%, and the F1-score reaches 96.74%. Compared with other mainstream semantic segmentation models, the proposed algorithm has the advantages of small parameters, high speed and high accuracy, which can provide a reference for the safe operation of unmanned vehicles in unstructured road scenarios.
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图 9 F1-score和loss训练过程曲线变化图
Figure 9. Variation curves of F1-score and loss during training
表 1 调整后的MobileNetV3-large特征提取网络
Table 1 MobileNetV3-large feature extraction network after adjustment
Input（H2×C） 操作 n SE 激活函数 s out 5122 × 3 Conv2d 1 - HS 2 16 2562 × 16 Bneck,3 × 3 1 - ReLU 1 16 2562 × 16 Bneck,3 × 3 1 - ReLU 2 24 1282 × 24 Bneck,3 × 3 1 - ReLU 1 24 1282 × 24 Bneck,5 × 5 1 √ ReLU 2 40 642 × 40 Bneck,5 × 5 2 √ ReLU 1 40 642 × 40 Bneck,3 × 3 1 - HS 2 80 322 × 80 Bneck,3 × 3 3 - HS 1 80 322 × 80 Bneck,3 × 3 2 √ HS 1 112 322 × 112 Bneck,5 × 5 1 √ HS 2 160 162 × 160 Bneck,5 × 5 2 √ HS 1 160 表中：H、C表示特征的边长、通道数；Conv2d表示标准卷积；n表示需进行操作的次数；SE表示是否引入SE注意力；s表示步长；out表示输出通道数。 
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表 2 不同改进措施的模型性能
Table 2 Model performance with different improvement measures
模型 MIOU/% F1-score/% Params Conv+Xception+FFM 89.83 92.66 1.54×106 Conv+ResNet+FFM 91.93 94.32 1.29×107 Conv+MobileNetv3+FFM 92.43 94.65 1.16×106 DSConv+MobileNetv3+FFM 92.68 94.80 1.09×106 DSConv+MobileNetv3+CAFFM 95.40 96.74 1.11×106
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表 3 不同网络模型性能对比
Table 3 Performance comparison of different network models
模型 F1-Score/% PA/% MIOU/% Params Speed/
f·s−1U-Net 91.56 95.67 89.38 1.94×106 26.17 DeepLabV3+ 95.85 97.46 94.23 3.87×107 15.21 FCN-32S 92.54 96.18 90.56 5.44×107 28.63 BiSeNet-resnet 94.32 96.27 91.93 4.92×107 23.57 BiSeNet-xception 92.66 95.31 89.83 1.54×106 30.96 本文 96.74 97.78 95.40 1.11×106 36.79
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