Distance Measurement Using a Stereo Vision System

Żak

2021

Remote Sensing

Self Cite

The inspection-class Remotely Operated Vehicles (ROVs) are crucial in underwater inspections. Their prime function is to allow the replacing of humans during risky subaquatic operations. These vehicles gather videos from underwater scenes that are sent online to a human operator who provides control. Furthermore, these videos are used for analysis. This demands an RGB camera operating at a close distance to the observed objects. Thus, to obtain a detailed depiction, the vehicle should move with a constant speed and a measured distance from the bottom. As very few inspection-class ROVs possess navigation systems that facilitate these requirements, this study had the objective of designing a vision-based control method to compensate for this limitation. To this end, a stereo vision system and image-feature matching and tracking techniques were employed. As these tasks are challenging in the underwater environment, we carried out analyses aimed at finding fast and reliable image-processing techniques. The analyses, through a sequence of experiments designed to test effectiveness, were carried out in a swimming pool using a VideoRay Pro 4 vehicle. The results indicate that the method under consideration enables automatic control of the vehicle, given that the image features are present in stereo-pair images as well as in consecutive frames captured by the left camera.

Section: Distortion Removalmentioning

confidence: 99%

Stereo Vision System for Vision-Based Control of Inspection-Class ROVs

Żak

2021

Remote Sensing

Self Cite

“…The remaining clusters are removed from the image. At the segmentation stage, the connected-component algorithm is the most applicable option to use [29]. Since the image contains only one cluster of pixels representing the land area, the pixels above that cluster are grouped together to constitute the sky area, and therefore are marked as such.…”

Section: Progressive Land Segmentationmentioning

confidence: 99%

Shoreline Detection and Land Segmentation for Autonomous Surface Vehicle Navigation with the Use of an Optical System

Zalewski

2020

Sensors

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Autonomous surface vehicles (ASVs) are a critical part of recent progressive marine technologies. Their development demands the capability of optical systems to understand and interpret the surrounding landscape. This capability plays an important role in the navigation of coastal areas a safe distance from land, which demands sophisticated image segmentation algorithms. For this purpose, some solutions, based on traditional image processing and neural networks, have been introduced. However, the solution of traditional image processing methods requires a set of parameters before execution, while the solution of a neural network demands a large database of labelled images. Our new solution, which avoids these drawbacks, is based on adaptive filtering and progressive segmentation. The adaptive filtering is deployed to suppress weak edges in the image, which is convenient for shoreline detection. Progressive segmentation is devoted to distinguishing the sky and land areas, using a probabilistic clustering model to improve performance. To verify the effectiveness of the proposed method, a set of images acquired from the vehicle’s operative camera were utilised. The results demonstrate that the proposed method performs with high accuracy regardless of distance from land or weather conditions.

“…A dynamic background model is one in which the background scenes may contain moving elements in an external environment, for example, moving grass in the wind. In the construction of a static representation of the scene, non-parametric statistical modelling of pixel process is used [6]. Background subtraction is a widely used approach for detecting moving objects using a static camera.…”

Section: B a C K G R O U N D S U B T R A C T I O Nmentioning

confidence: 99%

Detection of Unmanned Aerial Vehicles Using Computer Vision Methods: A Comparative Analysis

Scientific Journal of Polish Naval Academy

Przybysz

2020

Self Cite

Detection of small objects in the airspace is a crucial task in the military. In the era of today’s unmanned aerial vehicles (UAVs) technology, many military units are exposed to recognition and observation through flying objects. They are often equipped with optoelectronic warhead making a way to collect essential and secret data of the military unit. Modern technical solutions make it possible to implement some methods facilitating detection of flying objects. A lot of them utilize computer vision techniques based on image processing algorithm. Therefore, in this article, we present an analysis of the most promising algorithm for detection of small flying objects.