A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation
| Autor: | Bruno Ferreira, Nuno Cruz, António José Oliveira |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science Feature extraction Ocean Engineering Feature selection 02 engineering and technology Background noise lcsh:Oceanography 020901 industrial engineering & automation lcsh:VM1-989 Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Computer vision lcsh:GC1-1581 Underwater Water Science and Technology Civil and Structural Engineering Orb (optics) Feature detection (computer vision) business.industry lcsh:Naval architecture. Shipbuilding. Marine engineering n/a Feature (computer vision) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Journal of Marine Science and Engineering, Vol 9, Iss 361, p 361 (2021) Journal of Marine Science and Engineering Volume 9 Issue 4 |
| ISSN: | 2077-1312 |
| Popis: | In underwater navigation, sonars are useful sensing devices for operation in confined or structured environments, enabling the detection and identification of underwater environmental features through the acquisition of acoustic images. Nonetheless, in these environments, several problems affect their performance, such as background noise and multiple secondary echoes. In recent years, research has been conducted regarding the application of feature extraction algorithms to underwater acoustic images, with the purpose of achieving a robust solution for the detection and matching of environmental features. However, since these algorithms were originally developed for optical image analysis, conclusions in the literature diverge regarding their suitability to acoustic imaging. This article presents a detailed comparison between the SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and SURF-Harris algorithms, based on the performance of their feature detection and description procedures, when applied to acoustic data collected by an autonomous underwater vehicle. Several characteristics of the studied algorithms were taken into account, such as feature point distribution, feature detection accuracy, and feature description robustness. A possible adaptation of feature extraction procedures to acoustic imaging is further explored through the implementation of a feature selection module. The performed comparison has also provided evidence that further development of the current feature description methodologies might be required for underwater acoustic image analysis. |
| Databáze: | OpenAIRE |
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