دورية أكاديمية
Neural Network-Based Underwater Object Detection off the Coast of the Korean Peninsula
| العنوان: | Neural Network-Based Underwater Object Detection off the Coast of the Korean Peninsula |
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| المؤلفون: | Won-Ki Kim, Ho Seuk Bae, Su-Uk Son, Joung-Soo Park |
| المصدر: | Journal of Marine Science and Engineering, Vol 10, Iss 10, p 1436 (2022) |
| بيانات النشر: | MDPI AG, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Naval architecture. Shipbuilding. Marine engineering LCC:Oceanography |
| مصطلحات موضوعية: | underwater object detection, sonar image, sea experiment, deep learning, Naval architecture. Shipbuilding. Marine engineering, VM1-989, Oceanography, GC1-1581 |
| الوصف: | Recently, neural network-based deep learning techniques have been actively applied to detect underwater objects in sonar (sound navigation and ranging) images. However, unlike optical images, acquiring sonar images is extremely time- and cost-intensive, and therefore securing sonar data and conducting related research can be rather challenging. Here, a side-scan sonar was used to obtain sonar images to detect underwater objects off the coast of the Korean Peninsula. For the detection experiments, we used an underwater mock-up model with a similar size, shape, material, and acoustic characteristics to the target object that we wished to detect. We acquired various side-scan sonar images of the mock-up object against the background of mud, sand, and rock to account for the different characteristics of the coastal and seafloor environments of the Korean Peninsula. To construct a detection network suitable for the obtained sonar images from the experiment, the performance of five types of feature extraction networks and two types of optimizers was analyzed. From the analysis results, it was confirmed that performance was achieved when DarkNet-19 was used as the feature extraction network, and ADAM was applied as the optimizer. However, it is possible that there are feature extraction network and optimizer that are more suitable for our sonar images. Therefore, further research is needed. In addition, it is expected that the performance of the modified detection network can be more improved if additional images are obtained. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2077-1312 |
| Relation: | https://www.mdpi.com/2077-1312/10/10/1436; https://doaj.org/toc/2077-1312 |
| DOI: | 10.3390/jmse10101436 |
| URL الوصول: | https://doaj.org/article/49c1909b3b704a5c8561acefb92f5425 |
| رقم الأكسشن: | edsdoj.49c1909b3b704a5c8561acefb92f5425 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20771312 |
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| DOI: | 10.3390/jmse10101436 |