Volume 19, Issue 1 (March 2023)                   IJEEE 2023, 19(1): 2476-2476 | Back to browse issues page

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Ataee A, Kazemitabar S J. Real-Time YOLO Based Ship Detection Using Enriched Dataset. IJEEE 2023; 19 (1) :2476-2476
URL: http://ijeee.iust.ac.ir/article-1-2476-en.html
Real-Time YOLO Based Ship Detection Using Enriched Dataset
A. Ataee , S. J. Kazemitabar
Abstract:   (1401 Views)
We propose a real-time Yolov5 based deep convolutional neural network for detecting ships in the video. We begin with two famous publicly available SeaShip datasets each having around 9,000 images. We then supplement that with our self-collected dataset containing another thirteen thousand images. These images were labeled in six different classes, including passenger ships, military ships, cargo ships, container ships, fishing boats, and crane ships. The results confirm that Yolov5s can classify the ship's position in real-time from 135 frames per second videos with 99 % precision.
Keywords: convolutional neural network, Yolov5, object detection, ship detection
Full-Text [PDF 1236 kb]   (1230 Downloads)    
Type of Study: Research Paper | Subject: Image Processing
Received: 2022/03/28 | Revised: 2023/06/06 | Accepted: 2022/11/08
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.