Zhou Xiaoli, Bei Chao
Graduate School, The Second Research Academy of CASIC, Beijing, China.
CASIC Space Engineering Development Co., Ltd., Beijing, China.
PeerJ Comput Sci. 2024 Jul 12;10:e2192. doi: 10.7717/peerj-cs.2192. eCollection 2024.
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.
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.
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.
对于空间物体检测任务，传统光学相机面临各种应用挑战，包括背光问题和暗光条件。作为一种新型光学相机，事件相机由于其异步输出特性，具有高时间分辨率和高动态范围的优势，为上述挑战提供了新的解决方案。然而，事件相机的异步输出特性使其与为帧图像设计的传统物体检测方法不兼容。
提出了用于处理事件相机数据的异步卷积记忆网络（ACMNet），以解决背光和暗光空间物体检测问题。ACMNet的关键思想是首先通过指数核函数用事件尖峰张量（EST）体素网格对异步事件流进行特征化，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记忆模块ConvLSTM聚合时间特征，最后实现使用连续事件流的端到端物体检测。
在基于事件相机的大规模空间合成事件数据集Event_DVS_space7上，对ACMNet和经典物体检测方法进行了比较实验。结果表明，ACMNet的性能优于其他方法，在保持处理速度的同时，平均精度均值（mAP）提高了12.7%。此外，在传统光学相机失效的背光和暗光条件下，事件相机仍具有良好的性能。这项研究为复杂光照和运动条件下的检测提供了一种新的可能性，强调了事件相机在空间物体检测领域的优越优势。
