复杂场景下多尺度船舶实时检测方法

周薇娜; 刘露

doi:10.11959/j.issn.1000-0801.2022258

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复杂场景下多尺度船舶实时检测方法

研究与开发 | 更新时间：2024-06-05

- 复杂场景下多尺度船舶实时检测方法
- A real-time detection method for multi-scale ships in complex scenes
- 电信科学 2022年38卷第10期页码：67-78
- 作者机构：
- 作者简介：
  
  [ "周薇娜（1982- ），女，博士，上海海事大学信息工程学院副教授、硕士生导师，主要研究方向为图像处理、目标检测算法和ASIC设计" ]
  [ "刘露（1996- ），女，上海海事大学信息工程学院硕士生，主要研究方向为计算机视觉、目标检测与人工智能等" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(52071200);国家自然科学基金资助项目;The National Natural Science Foundation of China(61404083);专用集成电路与系统国家重点实验室开放研究课题基金资助项目;The State Key Laboratory of ASIC and System(2021KF010)
- DOI：10.11959/j.issn.1000-0801.2022258
  中图分类号： TP391
- 网络出版日期：2022-10，
  
  纸质出版日期：2022-10-20
- 稿件说明：
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周薇娜, 刘露. 复杂场景下多尺度船舶实时检测方法[J]. 电信科学, 2022,38(10):67-78.

Weina ZHOU, Lu LIU. A real-time detection method for multi-scale ships in complex scenes[J]. Telecommunications science, 2022, 38(10): 67-78.
周薇娜, 刘露. 复杂场景下多尺度船舶实时检测方法[J]. 电信科学, 2022,38(10):67-78. DOI： 10.11959/j.issn.1000-0801.2022258.

Weina ZHOU, Lu LIU. A real-time detection method for multi-scale ships in complex scenes[J]. Telecommunications science, 2022, 38(10): 67-78. DOI： 10.11959/j.issn.1000-0801.2022258.

摘要

船舶检测在军事侦察、海上目标跟踪、海上交通管制等任务中发挥着重要作用。然而，受船舶外形尺度多变和复杂海面背景的影响，在复杂海面上检测多尺度船舶仍然是一个挑战。针对此难题，提出了一种基于多层信息交互融合和注意力机制的 YOLOv4 改进方法。该方法主要通过多层信息交互融合（multi-layer information interactive fusion，MLIF）模块和多注意感受野（multi-attention receptive field，MARF）模块构建一个双向细粒度特征金字塔。其中，MLIF模块用于融合不同尺度的特征，不仅能将深层的高级语义特征串联在一起，而且将较浅层的丰富特征进行重塑；MARF由感受野模块（receptive field block，RFB）与注意力机制模块组成，能有效地强调重要特征并抑制冗余特征。此外，为了进一步评估提出方法的性能，在新加坡海事数据集（Singapore maritime dataset，SMD）上进行了实验。实验结果表明，所提方法能有效地解决复杂海洋环境下多尺度船舶检测的难题，且同时满足了实时需求。

Abstract

Ship detection plays an important role in tasks such as military reconnaissance

maritime target tracking

and maritime traffic control.However

due to the influence of variable sizes of ships and complex background of sea surface

detecting multi-scale ships remains a challenge in complex sea surfaces.To solve this problem

an improved YOLOv4 method based on multi-layers information interactive fusion and attention mechanism was proposed.Multi-layers information interactive fusion (MLIF) and multi-attention receptive field (MARF) were applied and combined reasonably to build a bidirectional fine-grained feature pyramid.MLIF was used to fuse feature of different scales

which not only concatenated high-level semantic features from deep layers

but also reshaped richer features from shallower layers.MARF consisted of receptive field block (RFB) and attention mechanism module

which effectively emphasized the important features and suppressed unnecessary ones.In addition

to further evaluate the performance of the proposed method

experiments were carried out on Singapore maritime dataset (SMD).The results illustrate that the method proposed can effectively solve the problem of difficult detection of multi-scale ships in complex marine environment

and meet the real-time requirements at the same time.

关键词

Keywords

references

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