Weak Moving Object Detection In Optical Remote Sensing Video With Motion-Drive Fusion Network

Object detection in optical remote sensing video (ORSV) is a new trend which makes it possible for obtaining richer information in more complicated and diverse situations. However, the small objects are blurred in the videos captured from optical sensor assembled in satellite, limited by the devices and natural weather. The concept of weak object is defined in this situation that the objects are extremely small and hardly detected with only one static image. Therefore, we propose a simple but efficient method for weak moving object detection in ORSV by combining the temporal information from neighbor frames and spatial features from image pixels. First, we compute the difference map between two adjacent frames, and stack it with original RGB channel so that a (1+3)-channel input data is made. Then, a motion-drive D-RGB (difference map with RGB image) fusion network is developed to obtain the feature map of this (1+3)-channel data. To adapt unusual scale in ORSV images, based on statistical prior objects size, we change the size of anchor box in original Faster R-CNN. The proposed method is demonstrated to improve the mean average precision on detecting weak moving remote sensing objects.