PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■对于空间物体探测任务，传统光学相机面临各种应用挑战，包括背光问题和昏暗的光线条件。作为一种新颖的光学相机，事件摄像机由于异步输出特性而具有高时间分辨率和高动态范围的优点，这为上述挑战提供了新的解决方案。然而,事件摄像机的异步输出特性使它们与为帧图像设计的常规目标检测方法不兼容。
■提出了用于处理事件摄像机数据的异步卷积存储器网络（ACMNet），以解决背光和昏暗空间物体检测的问题。ACMNet的关键思想是首先通过指数核函数用事件尖峰张量(EST)体素网格来表征异步事件流,然后使用前馈特征提取网络提取空间特征，并使用提出的卷积时空存储器模块ConvLSTM聚合时间特征，最后，实现了使用连续事件流的端到端对象检测。
■在Event_DVS_space7上进行了ACMNet和经典对象检测方法之间的比较实验，Event_DVS_space7是基于事件摄像机的大规模空间合成事件数据集。结果表明，ACMNet的性能优于其他ACMNet，mAP提高了12.7%，同时保持了处理速度。此外，事件摄像机在传统光学摄像机出现故障的背光和昏暗光线条件下仍然具有良好的性能。这项研究为在复杂的照明和运动条件下进行检测提供了一种新颖的可能性，强调事件相机在空间物体检测领域的优势。
■提出了用于处理事件摄像机数据的异步卷积存储器网络（ACMNet），以解决背光和昏暗空间物体检测的问题。ACMNet的关键思想是首先通过指数核函数用事件尖峰张量(EST)体素网格来表征异步事件流,然后使用前馈特征提取网络提取空间特征，并使用提出的卷积时空存储器模块ConvLSTM聚合时间特征，最后，实现了使用连续事件流的端到端对象检测。
■在Event_DVS_space7上进行了ACMNet和经典对象检测方法之间的比较实验，Event_DVS_space7是基于事件摄像机的大规模空间合成事件数据集。结果表明，ACMNet的性能优于其他ACMNet，mAP提高了12.7%，同时保持了处理速度。此外，事件摄像机在传统光学摄像机出现故障的背光和昏暗光线条件下仍然具有良好的性能。这项研究为在复杂的照明和运动条件下进行检测提供了一种新颖的可能性，强调事件相机在空间物体检测领域的优势。
