Sound event detection involves identifying sound categories in audio and determining when they start and end. However, in real-life situations, sound events are usually not isolated. When one sound event occurs, there are often other related sound events that take place as co-occurrences or successive occurrences. The timing relationship of sound events can reflect their characteristics. Therefore, this paper proposes a sound event detection method for traffic scenes based on a graph convolutional network, which considers this timing relationship as a form of multimodal information. The proposed method involves using the acoustic event window method to obtain co-occurrences or successive occurrences of relationship information in the sound signal while filtering out possible noise relationship information. This information is then represented as a graphical structure. Next, the graph convolutional neural network is improved to balance relationship weights between neighbors and itself and to avoid excessive smoothing. It is used to learn the relationship information in the graph structure. Finally, the convolutional recurrent neural network is used to learn the acoustic feature information of sound events, and the relationship information of sound events is obtained by multi-modal fusion to enhance the performance of sound event detection. The experimental results show that using multi-modal information with the proposed method can effectively improve the performance of the model and enhance the perception ability of smart cars in their surrounding environment while driving.

目的针对夜间低光照环境下目标特征退化与背景噪声导致跟踪器特征匹配失效及模板更新漂移的问题。提出一种融合黑暗感知提示的夜间目标跟踪框架（ProDAPT），不同于现有方法仅在输入端进行简单的像素级叠加或浅层特征注入，ProDAPT创新性地以冻结的Transformer为基础，构建全链路黑暗感知提示机制。方法首先，提出跨层层级一致性提示生成器（CTCP），利用迭代反向投影与跨层语义约束，在深层特征空间中强制恢复被噪声稀释的目标结构；其次，设计提示语义校准注意力（PSCA），通过提示特征的结构先验作为显式偏置校正Transformer的注意力分布，有效抑制夜间相似干扰物导致的注意力弥散；最后，提出能量感知双重门控更新策略（EDGU），利用提示能量作为独立于分类分数的结构完整性的度量指标，实现更加可靠的动态模板更新。结果在NAT2021、LLOT和UAVDark135三个主流基准数据集及自采的4K分辨率自动驾驶跟车数据集上进行实验。该算法在三个公开数据集上的成功率分别达到0.557、0.585和0.608，其中在NAT2021上相比同类提示学习方法DCPT提升了3.1%；在NVIDIA A100硬件上的推理速度达到76.8FPS，参数量仅为全量微调的7.03%，并在自采数据集的真实夜间场景中也展示了良好跟踪效果。结论该方法通过全链路黑暗感知机制，有效增强了夜间特征鉴别力与更新可靠性，为解决夜间视觉跟踪任务中的感知与决策断层问题提供了新的有效途径。