eprintid: 10199771
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/19/97/71
datestamp: 2024-11-08 08:42:11
lastmod: 2024-11-08 08:42:11
status_changed: 2024-11-08 08:42:11
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Shao, Zeyuan
creators_name: Yin, Yong
creators_name: Lyu, Hongguang
creators_name: Soares, C Guedes
creators_name: Cheng, Tao
creators_name: Jing, Qianfeng
creators_name: Yang, Zhilin
title: An efficient model for small object detection in the maritime environment
ispublished: pub
divisions: UCL
divisions: B04
divisions: F44
keywords: Autonomous navigation, Sea surface, Object detection Deep learning, Model quantization
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
abstract: Environmental perception is crucial for autonomous ships realizing autonomous navigation, in particular, the high-precision and low-latency detection of small objects on the sea surface is a key and challenging issue. To address this problem, this paper presents a model that improves the detection accuracy and delivers excellent real-time performance for autonomous ship navigation. The backbone of the proposed model enhances the modelling capabilities by expanding deformable convolution and introducing the self-designed attention mechanism. Additionally, an enhanced feature fusion structure is designed by the pixel shuffle based on super-resolution reconstruction to keep the integrity of feature information for small objects. This paper also presents an optimized model quantization strategy that alleviates the problem of low model efficiency caused by the limited resources onboard the ship. Compared to the earlier model, the present one has increased the mean average precision on the Rizhao Zhuimeng-3# maritime optical dataset by 4.5 % and by 21 % for small object detection. Furthermore, the real-time detection for high-resolution images can now reach a speed of 67 frames/s. Moreover, the present model outperforms existing methods concerning accuracy when the frames/s are similar. The results indicate that the proposed model can be potentially applied to autonomous ships.
date: 2024-11
date_type: published
publisher: Elsevier
official_url: http://dx.doi.org/10.1016/j.apor.2024.104194
full_text_type: other
language: eng
verified: verified_manual
elements_id: 2311536
doi: 10.1016/j.apor.2024.104194
lyricists_name: Cheng, Tao
lyricists_id: TCHEN23
actors_name: Cheng, Tao
actors_id: TCHEN23
actors_role: owner
funding_acknowledgements: 2022YFB4300803 [National Key R & D Program of China]; 2022YFB4301402 [National Key R & D Program of China]; 52071049 [National Natural Science Foundation of China]; 2022JH1/10800096 [Liaoning Provincial Science and Technology Plan (Key) project]; CSC [2022] 2260 [Chinese Scholarships Council]; UIDB/UIDP/00134/2020 [Portuguese Foundation for Science and Technology (Fundaca o para a Ciencia e Tecnologia-FCT)]
full_text_status: restricted
publication: Applied Ocean Research
volume: 152
article_number: 104194
pages: 16
citation:        Shao, Zeyuan;    Yin, Yong;    Lyu, Hongguang;    Soares, C Guedes;    Cheng, Tao;    Jing, Qianfeng;    Yang, Zhilin;      (2024)    An efficient model for small object detection in the maritime environment.                   Applied Ocean Research , 152     , Article 104194.  10.1016/j.apor.2024.104194 <https://doi.org/10.1016/j.apor.2024.104194>.      
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10199771/1/An%20Efficient%20Model%20for%20Small%20Object%20Detection%20in%20the%20Maritime%20Environment.pdf