Raw data-code-ACMNet

Background. For space object detection tasks, conventional optical cameras face various application challenges, including backlight issues and dim light conditions. As a novel optical camera, the event camera has the advantages of high temporal resolution and high dynamic range due to asynchronous output characteristics, which provides a new solution to the above challenges. However, the asynchronous output characteristic of event cameras makes them incompatible with conventional object detection methods designed for frame images.Methods. Asynchronous convolutional memory network (ACMNet) for processing event camera data is proposed to solve the problem of backlight and dim space object detection. The key idea of ACMNet is to first characterize the asynchronous event streams with the Event Spike Tensor (EST) voxel grid through the exponential kernel function, then extract spatial features using a feed-forward feature extraction network, and aggregate temporal features using a proposed convolutional spatiotemporal memory module ConvLSTM, and finally, the end-to-end object detection using continuous event streams is realized.Results. Comparison experiments among ACMNet and classical object detection methods are carried out on Event_DVS_space7, which is a large-scale space synthetic event dataset based on event cameras. The results show that the performance of ACMNet is superior to the others, and the mAP is improved by 12.7% while maintaining the processing speed. Moreover, event cameras still have a good performance in backlight and dim light conditions where conventional optical cameras fail. This research offers a novel possibility for detection under intricate lighting and motion conditions, emphasizing the superior benefits of event cameras in the realm of space object detection.

背景：针对空间目标检测任务，传统光学相机面临诸多应用难题，涵盖逆光场景与低光照环境。事件相机（event camera）作为一类新型光学相机，依托异步输出特性具备高时间分辨率与高动态范围的优势，为破解上述难题提供了全新解决方案。然而，事件相机的异步输出特性使其无法适配针对帧图像设计的传统目标检测方法。

方法：为解决逆光与低光照条件下的空间目标检测难题，本文提出了用于处理事件相机数据的异步卷积记忆网络（Asynchronous Convolutional Memory Network, ACMNet）。该网络的核心设计思路为：首先通过指数核函数将异步事件流表征为事件尖峰张量（Event Spike Tensor, EST）体素网格，随后利用前馈特征提取网络提取空间特征，并通过本文提出的卷积长短期记忆网络（ConvLSTM）型卷积时空记忆模块聚合时间特征，最终实现基于连续事件流的端到端目标检测。

结果：在基于事件相机构建的大规模空间合成事件数据集Event_DVS_space7上，开展了ACMNet与经典目标检测方法的对比实验。实验结果表明，ACMNet的性能优于其余对比方法，在保持处理速度不变的前提下，平均精度均值（mean Average Precision, mAP）提升了12.7%。此外，在传统光学相机难以胜任的逆光与低光照场景中，事件相机仍可保持良好的检测性能。本研究为复杂光照与运动条件下的目标检测提供了全新可能，凸显了事件相机在空间目标检测领域的显著优势。