Evidence map›Paper›PMID 39896402›Full record

ArticlePeerJ. Computer science2024

Ship detection based on semantic aggregation for video surveillance images with complex backgrounds.

Yongmei Ren, Haibo Liu, Jie Yang, Xiaohu Wang, Wei He, Dongrui Xiao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yongmei RenSchool of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, China.
Haibo LiuSchool of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, China.
Jie YangSchool of Information Engineering, Wuhan University of Technology, Wuhan, China.
Xiaohu WangCollege of Intelligent Manufacturing and Mechanical Engineering, Hunan Institute of Technology, Hengyang, China.
Wei HeSchool of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, China.
Dongrui XiaoSchool of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ship detection in video surveillance images holds significant practical value. However, the background in these images is often complex, complicating the achievement of an optimal balance between detection precision and speed. Method: This study proposes a ship detection method that leverages semantic aggregation in complex backgrounds. Initially, a semantic aggregation module merges deep features, rich in semantic information, with shallow features abundant in location details, extracted Results: The experimental results on the SeaShips dataset and SeaShips_enlarge dataset demonstrate that the mean average precision@0.5 (mAP@0.5) of this proposed method reaches 89.30% and 89.10%, respectively. Conclusions: The proposed method surpasses other existing ship detection techniques in terms of detection effect and meets real-time detection requirements, underscoring its engineering relevance.

Indexed as

Feature fusionImage processingSemantic aggregationShip detectionVideo surveillance images

Identifiers

PMID39896402
PMCPMC11784822

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.