基于STOD的无人机舰船图像微小目标检测方法

doi:10.12305/j.issn.1001-506X.2025.11.05

Abstract

Abstract:

A ship tiny object detection（STOD ）is proposed to address the problem of high proportion of small targets and difficult detection in unmanned aerial vehicle ship images. The algorithm is based on image characteristics, ship features, and combined with target specific scenes, focusing on the difficult problems in unmanned aerial vehicle STOD process. A targeted algorithm model and network framework are designed. Firstly, a lightweight single-stage anchor free network is adopted as the backbone, followed by the introduction of a global attention mechanism to enhance the model’s learning ability for tiny objects. Then, cross-stage partial pyramid aggregation network is designed and the detection head is strengthened to improve the network’s ability to extract target features and detection accuracy. Finally, a vector based distribution focus loss is used in the regression branch to further enhance the detection ability for tiny objects. Experimental verification and comparative analysis indicate that the proposed method improves the detection performance of tiny objects on ships by 2.6% compared to the baseline model average precision, and the frame per second can reach 52.2. Compared with other small target algorithms, it has high detection accuracy and good real-time performance.

Key words: unmanned aerial vehicle （UAV）, ship image, tiny objects detection, deep learning, global attention mechanism

CLC Number:

TP 391.4

Jie JIANG, Wenjun YAN, Qing LING, Limin ZHANG. Tiny objects detection method for unmanned aerial vehicle ship images based on STOD[J]. Systems Engineering and Electronics, 2025, 47(11): 3559-3567.

Figures/Tables 15

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序号	类别	数量
1	中目标	7541
2	小目标	34215
3	微小目标	25359

模块	AP/%	参数量（×10⁶）	运算量/ GFLOPs	时延/ ms	FPS
基线模型	47.4	50.63	110.75	12.9	55.4
增加无锚框	47.1（−0.3）	48.54	108.47	12.1	56.3
改进网络结构	47.5（+0.4）	46.32	104.51	11.2	58.7
+全局注意力模块	48.0（+0.5）	48.42	106.87	12.2	56.1
+CSPPAN	48.7（+0.7）	52.31	110.07	13.1	55.3
ET-head	49.6（+0.9）	53.68	114.78	14.1	53.3
+基于向量DFL	50.1（+0.5）	55.58	115.77	15.3	52.2

参数组合	AP/%	AP₅₀ /%	推理速度/（帧/s）
α=1.0, β=0.5, γ=0.5	33.2	56.7	53.1
α=1.2, β=0.6, γ=0.4	33.8	58.3	52.7
α=0.8, β=1.0, γ=0.3	33.1	55.2	53.6

算法名称	AP	AP₅₀	AP₇₅	AP_T	AP_S	AP_M
扩展FPN	29.13	54.07	27.38	29.64	32.98	77.13
大核卷积与层次特征融合	28.55	56.63	26.68	29.68	33.15	74.51
多尺度特征提取与跨阶段特征融合网络	27.33	49.56	26.55	32.13	36.84	78.01
密集卷积注意力网络	26.46	48.34	25.38	31.45	35.71	76.49
多重注意力轻量化网络	26.35	47.98	25.45	32.09	36.78	76.17
YOLOX	31.28	60.39	29.61	32.15	37.14	77.84
YOLOv8-p2	33.52	62.41	32.16	32.44	37.79	78.11
YOLOv9	33.64	62.56	32.45	32.49	37.83	78.31
YOLOv10	33.84	62.78	32.31	32.61	38.02	78.40
本文算法	34.61	63.25	32.54	32.72	38.16	78.47

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Tiny objects detection method for unmanned aerial vehicle ship images based on STOD

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