Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal IWTJ : International Water Transport Journal
Ulya, Febri Himmatul
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Transportation

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : IWTJ : International Water Transport Journal

 1 
Comparison Of SSD Method Performance with YOLOv7 in Detecting River And Lake Traffic Signs Amrillah, Muhammad Fahmi; Sari, Desti Yuvita; Utami, Fisca Dian; Sari, Yulia Puspita; Ulya, Febri Himmatul; Yuda, Dimas Pratama
IWTJ : International Water Transport Journal Vol. 7 No. 1 (2025): International Water Transport Journal (IWTJ:April)
Publisher : Politeknik Transportasi Sungai Danau dan Penyeberangan Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

River and lake traffic signs play an important role in maintaining the safety of water navigation, but there is still a lack of understanding and supporting technology to recognize them automatically. This study aims to compare the performance of two popular object detection methods, namely YOLOv7 and Single Shot Multibox Detector (SSD), in identifying river and lake signs based on digital images. The dataset used consists of 6,650 images, including original data and data that has been augmented with visual degradation such as low resolution, noise, blur, lighting artifacts, occlusion, and cluttered background. This study evaluates the models based on accuracy, precision, and recall metrics. The test results show that YOLOv7 excels in all metrics, with the highest accuracy of 78%, compared to SSD which only reaches 70%. Both models experience a significant decrease in performance under occlusion and cluttered background conditions, but YOLOv7 still shows better resilience. Meanwhile, the blur condition is the degradation that least affects the performance of both models. These findings confirm that YOLOv7 is more robust to visual disturbances and can be a more effective alternative in automatic river and lake traffic sign recognition systems.
Co-Authors Amrillah, Muhammad Fahmi Dimas Pratama Yuda, Dimas Sari, Desti Yuvita Sari, Yulia Puspita Utami, Fisca Dian