Pablo Alonso, Jon Ander Íñiguez de Gordoa, Juan Diego Ortega, Marcos Nieto
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Efficient class-agnostic obstacle detection for UAV-assisted waterway inspection systems
Ensuring the safety of water airport runways is essential for the correct operation of seaplane flights. Among other tasks, airport operators must identify and remove various objects that may have drifted into the runway area. In this paper, the authors propose a complete and embedded-friendly waterway obstacle detection pipeline that runs on a camera-equipped drone. This system uses a class-agnostic version of the YOLOv7 detector, which is capable of detecting objects regardless of its class. Additionally, through the usage of the GPS data of the drone and camera parameters, the location of the objects are pinpointed with 0.58 m Distance Root Mean Square. In our own annotated dataset, the system is capable of generating alerts for detected objects with a recall of 0.833 and a precision of 1.
期刊介绍：
IET Computer Vision seeks original research papers in a wide range of areas of computer vision. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in computer vision.
IET Computer Vision welcomes submissions on the following topics:
Biologically and perceptually motivated approaches to low level vision (feature detection, etc.);
Perceptual grouping and organisation
Representation, analysis and matching of 2D and 3D shape
Shape-from-X
Object recognition
Image understanding
Learning with visual inputs
Motion analysis and object tracking
Multiview scene analysis
Cognitive approaches in low, mid and high level vision
Control in visual systems
Colour, reflectance and light
Statistical and probabilistic models
Face and gesture
Surveillance
Biometrics and security
Robotics
Vehicle guidance
Automatic model aquisition
Medical image analysis and understanding
Aerial scene analysis and remote sensing
Deep learning models in computer vision
Both methodological and applications orientated papers are welcome.
Manuscripts submitted are expected to include a detailed and analytical review of the literature and state-of-the-art exposition of the original proposed research and its methodology, its thorough experimental evaluation, and last but not least, comparative evaluation against relevant and state-of-the-art methods. Submissions not abiding by these minimum requirements may be returned to authors without being sent to review.
Special Issues Current Call for Papers:
Computer Vision for Smart Cameras and Camera Networks - https://digital-library.theiet.org/files/IET_CVI_SC.pdf
Computer Vision for the Creative Industries - https://digital-library.theiet.org/files/IET_CVI_CVCI.pdf
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