Image-based obstacle detection methods are crucial for the safe navigation of autonomous robotic vessels. Current state-of-the-art method WaSR performs obstacle detection with semantic segmentation of current image while using information from inertial sensor. However, like related methods, it has problems with small objects, glare and reflections. In this thesis, we propose a method which takes into account movement to resolve visual uncertainty in the segmentation of current image. We propose a process that enriches the appearance of the image element in the current image with past appearances by aligning past images. The enriched image is then segmented with a minimally modified WaSR model. The correspondences between current image elements and ones in previous images are calculated using currently one of the most successful methods of calculating optical flow RAFT. Several variants of using enriched visual information are presented. The results of the evaluations show similar F_1 measure of the WaSR_Mu model compared to the original one while taking into account all obstacles and comparable detections of the water edge. The main achievement is 12% imporvement in detecting obsticles within the danger zone mainly due to smaller number of false-positive detections under mentioned difficult conditions.