Abstract
Event-based Vision Sensors (EVS) have demonstrated significant advantages over traditional RGB frame-based cameras in low-light conditions, high-speed motion capture, and low latency. Consequently, object detection based on EVS has attracted increasing attention from researchers. Current event stream object detection algorithms are typically built upon Convolutional Neural Networks (CNNs) or Transformers, which either capture limited local features using convolutional filters or incur high computational costs due to the utilization of self-attention. Recently proposed vision heat conduction backbone networks have shown a good balance between efficiency and accuracy; however, these models are not specifically designed for event stream data. They exhibit weak capability in modeling object contour information and fail to exploit the benefits of multi-scale features. To address these issues, this paper proposes a novel dynamic graph induced contour-aware heat conduction network for event stream based object detection, termed CvHeat-DET. The proposed model effectively leverages the clear contour information inherent in event streams to predict the thermal diffusivity coefficients within the heat conduction model, and integrates hierarchical structural graph features to enhance feature learning across multiple scales. Extensive experiments on three benchmark datasets for event stream-based object detection fully validated the effectiveness of the proposed model. The source code of this paper will be released on this https URL.
Abstract (translated)
基于事件的视觉传感器（EVS）在低光环境、高速运动捕捉和低延迟方面展示了相对于传统RGB帧相机的巨大优势。因此，基于EVS的对象检测吸引了越来越多的研究人员的关注。目前，基于事件流的对象检测算法通常建立在卷积神经网络（CNN）或Transformer的基础上，前者利用卷积滤波器捕获有限的局部特征，而后者则由于使用自注意力机制而导致计算成本高昂。最近提出的视觉热传导骨干网表现出效率和准确性之间的良好平衡；然而，这些模型并没有专门针对事件流数据进行设计，因此在建模物体轮廓信息方面能力较弱，并且未能充分利用多尺度特性带来的优势。 为了解决这些问题，本文提出了一种新颖的动态图诱导的感知轮廓的热传导网络用于基于事件流的对象检测，称为CvHeat-DET。所提出的模型有效利用了事件流中固有的清晰轮廓信息来预测热传导模型中的热扩散系数，并通过整合分层结构化图形特征来增强多尺度特征学习。在三个基准数据集上进行的广泛实验全面验证了所提出模型的有效性。 该论文的源代码将在此网址发布：[https URL]（请根据实际情况替换为具体的URL）。
URL
https://arxiv.org/abs/2505.12908
