改进Yolact模型，提升铁路场景实时分割的边缘精度。
RailYolact -- A Yolact Focused on edge for Real-Time Rail Segmentation
- 在损失函数中引入边缘信息，强化模型对铁轨边界的关注。
- 训练时用盒滤波平滑标签边缘，使预测结果更平滑。
- 保持推理速度不变，适合部署于边缘设备的实时铁路分割任务。
确保自动驾驶列车在轨道上避障至关重要，其第一步是准确分割轨道区域。本文基于Yolact框架进行改进，针对模型预测的轨道掩码边缘粗糙的问题，将边缘算子提取的边缘信息融入原始Yolact的损失函数，以增强模型对轨道边界的关注。同时，采用盒滤波对线性插值导致的锯齿状真实标签边缘进行平滑处理。由于边缘信息融合与平滑过程仅在训练阶段执行，模型推理速度不受影响。在自建铁路数据集上的实验表明，预测精度得到提升；在Cityscapes数据集上，$AP$和$AP_{50}$分别提升了4.1和4.6，优于原始Yolact。
原文摘要 · Abstract (English)
Ensuring obstacle avoidance on the rail surface is crucial for the safety of autonomous driving trains and its first step is to segment the regions of the rail. We chose to build upon Yolact for our work. To address the issue of rough edge in the rail masks predicted by the model, we incorporated the edge information extracted by edge operator into the original Yolact's loss function to emphasize the model's focus on rail edges. Additionally, we applied box filter to smooth the jagged ground truth mask edges cause by linear interpolation. Since the integration of edge information and smooth process only occurred during the training process, the inference speed of the model remained unaffected. The experiments results on our custom rail dataset demonstrated an improvement in the prediction accuracy. Moreover, the results on Cityscapes showed a 4.1 and 4.6 improvement in $AP$ and $AP_{50}$ , respectively, compared to Yolact.
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