复杂场景下多尺度船舶实时检测方法

doi:10.11959/j.issn.1000-0801.2022258

Abstract

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.

Key words: multi-scale ship detection, multi-layers information interactive fusion, multi-attention receptive field, bidirectional fine-grained feature pyramid

CLC Number:

TP391

Weina ZHOU, Lu LIU. A real-time detection method for multi-scale ships in complex scenes[J]. Telecommunications Science, 2022, 38(10): 67-78.

Figures/Tables 11

对比项		mAP		FPS
对比项	大尺寸	中尺寸	小尺寸	FPS
FPN-PAN	0.675	0.375	0.192	27.2
MLIF-PAN	0.749	0.665	0.511	25.1
本文算法（MLIF-MARF）	$0 . 781$	$0 . 679$	$0 . 601$	26.0

方法	R	P	F1	mAP	FPS
Faster-RCNN^[7]	0.741	0.851	0.786	0.775	7.3
RetinaNet^[22]	$0 . 696$	0.784	0.731	0.703	22.9
SSD^[11]	0.306	0.768	0.403	0.473	40.0
CenterNet^[24]	0.673	0.794	0.712	0.688	52.3
YOLOx^[23]	0.644	0.775	0.689	0.670	$59 . 9$
YOLOv3^[10]	0.512	0.780	0.593	0.579	10.4
YOLOv4^[21]	0.618	0.747	0.661	0.648	27.2
本文算法	0.678	$0 . 865$	$0 . 744$	$0 . 769$	26.0

References 30

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FPN	PAN	MLIF	MARF		mAP	FPS
FPN	PAN	MLIF	RFB	SA,CA	mAP	FPS
√	√				0.648	27.2
	√	√			0.696	27.1
√			√		0.684	27.5
√				√	0.632	27.7
√			√	√	0.701	26.5
		√	√		0.733	26.1
		√		√	0.704	26.3
		√	√	√	0.769	26.0

A real-time detection method for multi-scale ships in complex scenes

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