An Efficient Module for Instance Segmentation Based on Multi-Level Features and Attention Mechanisms

Featured ApplicationThe proposed method has a potential application value in assisting the normal driving of unmanned delivery vehicles and unmanned cleaning vehicles in urban street scenes. It can aid unmanned vehicles to detect and segment surrounding objects and plan safe driving routes to avoid obstacles according to the results of instance segmentation.Recently, multi-level feature networks have been extensively used in instance segmentation. However, because not all features are beneficial to instance segmentation tasks, the performance of networks cannot be adequately improved by synthesizing multi-level convolutional features indiscriminately. In order to solve the problem, an attention-based feature pyramid module (AFPM) is proposed, which integrates the attention mechanism on the basis of a multi-level feature pyramid network to efficiently and pertinently extract the high-level semantic features and low-level spatial structure features; for instance, segmentation. Firstly, we adopt a convolutional block attention module (CBAM) into feature extraction, and sequentially generate attention maps which focus on instance-related features along the channel and spatial dimensions. Secondly, we build inter-dimensional dependencies through a convolutional triplet attention module (CTAM) in lateral attention connections, which is used to propagate a helpful semantic feature map and filter redundant informative features irrelevant to instance objects. Finally, we construct branches for feature enhancement to strengthen detailed information to boost the entire feature hierarchy of the network. The experimental results on the Cityscapes dataset manifest that the proposed module outperforms other excellent methods under different evaluation metrics and effectively upgrades the performance of the instance segmentation method.

特色应用 所提出的方法在辅助城市街景中的无人配送车和无人清扫车正常行驶方面具有潜在应用价值。它可以帮助无人车检测和分割周围物体，并根据实例分割结果规划安全行驶路线以避开障碍物。 最近，多级特征网络在实例分割中得到了广泛应用。然而，由于并非所有特征都对实例分割任务有益，不加区分地合成多级卷积特征无法充分提高网络性能。为了解决这一问题，提出了一种基于注意力的特征金字塔模块（AFPM），它在多级特征金字塔网络的基础上集成注意力机制，以便高效且有针对性地提取用于实例分割的高级语义特征和低级空间结构特征。首先，我们在特征提取中采用卷积块注意力模块（CBAM），并依次沿通道和空间维度生成关注实例相关特征的注意力图。其次，我们通过横向注意力连接中的卷积三元组注意力模块（CTAM）建立维度间的依赖关系，用于传播有用的语义特征图并过滤与实例对象无关的冗余信息特征。最后，我们构建用于特征增强的分支以强化细节信息，从而提升网络的整个特征层次结构。在Cityscapes数据集上的实验结果表明，所提出的模块在不同评估指标下优于其他优秀方法，并有效提升了实例分割方法的性能。