The Hull Structure and Defect Detection Based on Improved YOLOv5 for Mobile Platform

Jian Zhou, Weixing Li, Haoyu Fang, Yu Zhang, Feng Pan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Hull inspection is of great significance to ensure the safety of ships for ocean transportation. Improving the intelligent performance of inspection can benefit the efficiency of hull inspection. In this paper, we propose a detection method for hull structure and defect using improved YOLOv5. Residual connections and weighted feature fusion are adopted to learn the importance of different input features, which enhance feature expression and improve the efficiency. Coupled with transformer block, we further capture global information and abundant contextual information. For the convenience of detection, we convert the detection model and deploy it on mobile devices. The detection model is packaged into apk, which is installed on the mobile platform to realize the hull image detection. Extensive experiments show that our improved YOLOv5 achieves better performance than the original algorithm on our hull structure and defect datasets. The results of mAP are 82.5% and 65.8%, which are improved by 3.7% and 2.8% respectively. Based on mobile deployment, the high accuracy of mobile detection demonstrates that our work is of great significance for the research of ship inspection.

Original languageEnglish
Title of host publicationProceedings of the 41st Chinese Control Conference, CCC 2022
EditorsZhijun Li, Jian Sun
PublisherIEEE Computer Society
Pages6392-6397
Number of pages6
ISBN (Electronic)9789887581536
DOIs
Publication statusPublished - 2022
Event41st Chinese Control Conference, CCC 2022 - Hefei, China
Duration: 25 Jul 202227 Jul 2022

Publication series

NameChinese Control Conference, CCC
Volume2022-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference41st Chinese Control Conference, CCC 2022
Country/TerritoryChina
CityHefei
Period25/07/2227/07/22

Keywords

  • Hull Inspection
  • Improved YOLOv5
  • Mobile Deployment
  • Weighted Feature Fusion

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