Maritime ship detection algorithm under complex weather environments based on enhanced YOLOv8

doi:10.11772/j.issn.1001-9081.2025060723

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (6): 1998-2006.DOI: 10.11772/j.issn.1001-9081.2025060723

• Multimedia computing and computer simulation • Previous Articles

Maritime ship detection algorithm under complex weather environments based on enhanced YOLOv8

Zhenkai XIONG¹^,²(), Mengjun XU¹^,², Yinyin SUN³, Xin WANG⁴

^1.College of New Energy and Intelligent Connected Vehicle，Anhui University of Science and Technology，Hefei Anhui 231131，China
^2.Institute of Special Vehicles and Unmanned Systems，Anhui University of Science and Technology，Hefei Anhui 231131，China
^3.CAERI Intelligent Network Technology Company Limited，Chongqing 401100，China
^4.Northwest Institute of Mechanical and Electrical Engineering，Xianyang Shaanxi 712000，China

Received:2025-06-30 Revised:2025-09-24 Accepted:2025-09-26 Online:2025-10-21 Published:2026-06-10
Contact: Zhenkai XIONG
About author:XU Mengjun， born in 2001， M. S. candidate. His research interests include machine vision， artificial intelligence.
SUN Yinyin， born in 1986， M. S.， senior engineer. His research interests include vehicle autonomous driving.
WANG Xin， born in 1979， senior engineer. His research interests include electromechanical control.
First author contact:XIONG Zhenkai， born in 1979， Ph. D.， professor. His research interests include unmanned systems， special vehicles.
Supported by:
High-level Talent Introduction Program of Anhui University of Science and Technology(2023yjrc55)

基于改进YOLOv8的复杂天气环境下海面船舶检测算法

熊珍凯¹^,²(), 徐梦军¹^,², 孙胤胤³, 王鑫⁴

^1.安徽理工大学新能源与智能网联汽车学院，合肥 231131
^2.安徽理工大学特种车辆及无人系统研究所，合肥 231131
^3.中汽院智能网联科技有限公司，重庆 401100
^4.西北机电工程研究所，陕西咸阳 712000

通讯作者: 熊珍凯
作者简介:徐梦军（2001—），男，安徽阜阳人，硕士研究生，主要研究方向：机器视觉、人工智能
孙胤胤（1986—），男，江苏徐州人，硕士，高级工程师，主要研究方向：车辆自动驾驶
王鑫（1979—），男，陕西咸阳人，高级工程师，主要研究方向：机电控制。
第一联系人：熊珍凯（1979—），男，湖北天门人，教授，博士，主要研究方向：无人系统、特种车辆
基金资助:
安徽理工大学高层次人才引进基金资助项目(2023yjrc55)

Abstract

Abstract:

To address the problems of missed detection and false detection in maritime ship detection tasks under complex weather environments such as rain， fog， and low light， a maritime ship detection algorithm based on enhanced YOLOv8 under complex weather environments was proposed. Firstly， a Cross-Granularity Local Global Attention Fusion Block （CGLGAFB） was proposed， so that a refined local and global feature fusion mechanism was constructed and multi-path feature fusion strategies were combined to integrate multi-source feature information from different levels， thereby enhancing the model’s feature fusion capability as well as suppressing noise interference and information redundancy. Then， the original C2f （Faster Implementation of CSP Bottleneck with 2 convolutions） module was improved to an adaptive mixed C2f module （C2f-Adaptive Mixer Block， C2f-AMB）. The target features of different scales and complex spatial structure were captured by the model more flexibly and efficiently through deep convolution branches with adaptive receptive field adjustment capability， thereby enhancing feature extraction capabilities. Finally， a Multi-scale Spatial Perception Pyramid （MSPP） module was proposed to replace SPPF（Spatial Pyramid Pooling-Fast） module， so that dilated convolutions with different dilation rates were utilized to construct multi-scale receptive fields， thereby obtaining comprehensive contextual information， and reducing the omission of key information. Experimental results on the enhanced dataset SeaShips_aug show that the proposed algorithm achieves the mAP@50 and recall of 84.7% and 79.3%， respectively， which are 2.6 and 3.9 percentage points higher than those of the baseline model YOLOv8， respectively， verifying that the proposed algorithm is more suitable for maritime ship detection tasks under complex weather environments.

Key words: complex weather environment, YOLOv8, deep learning, ship detection, object recognition

摘要：

针对海面船舶检测任务在雨雾和低光照等复杂天气环境下存在的漏检与误检问题，提出一种基于改进YOLOv8的复杂天气环境海面船舶检测算法。首先，提出跨层次局部与全局注意力融合模块（CGLGAFB），通过构建精细化的局部和全局特征融合机制，并结合多路特征融合策略整合来自不同层次的多源特征信息，提升模型的特征融合能力，抑制噪声干扰与信息冗余；其次，改进原C2f （Faster Implementation of CSP Bottleneck with 2 convolutions）模块为自适应混合C2f模块（C2f-AMB），通过有自适应感受野调节能力的深度卷积分支，使模型能够更灵活、更高效地捕获不同尺度与复杂空间结构的目标特征，增强特征提取能力；最后，提出多尺度空间感知金字塔（MSPP）模块替换SPPF（Spatial Pyramid Pooling-Fast）模块，利用不同空洞率的空洞卷积构建多尺度感受野，获取全面的上下文信息，减少关键信息遗漏。在增强后的数据集SeaShips_aug上的实验结果表明，所提算法的mAP@50和召回率分别达到84.7%和79.3%，比基线模型YOLOv8分别高了2.6和3.9个百分点，验证了所提算法更适合复杂天气环境下的海面船舶检测任务。

关键词: 复杂天气环境, YOLOv8, 深度学习, 船舶检测, 目标识别

CLC Number:

TP391.4

Zhenkai XIONG, Mengjun XU, Yinyin SUN, Xin WANG. Maritime ship detection algorithm under complex weather environments based on enhanced YOLOv8[J]. Journal of Computer Applications, 2026, 46(6): 1998-2006.

熊珍凯, 徐梦军, 孙胤胤, 王鑫. 基于改进YOLOv8的复杂天气环境下海面船舶检测算法[J]. 《计算机应用》唯一官方网站, 2026, 46(6): 1998-2006.

Figures/Tables 12

References 29

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参数	值	参数	值
输入尺寸	640×640	优化器	SGD
初始学习率	1×10^-2	动量	0.937
迭代次数	150	线程数	4
批量大小	32	热身轮数	3

参数	值	参数	值
输入尺寸	640×640	优化器	SGD
初始学习率	1×10^-2	动量	0.937
迭代次数	150	线程数	4
批量大小	32	热身轮数	3

Baseline	①	②	③	mAP@50/ %	mAP@50-95/ %	Params	FLOPs/ 10⁹	Size/MB
√	×	×	×	0.821	0.563	3 006 818	8.1	6.2
√	√	×	×	0.839	0.580	3 807 842	9.3	8.0
√	×	√	×	0.837	0.554	2 339 190	6.3	4.8
√	×	×	√	0.834	0.568	3 154 274	8.1	6.5
√	√	√	√	0.847	0.571	3 287 670	7.5	6.7

Baseline	①	②	③	mAP@50/ %	mAP@50-95/ %	Params	FLOPs/ 10⁹	Size/MB
√	×	×	×	0.821	0.563	3 006 818	8.1	6.2
√	√	×	×	0.839	0.580	3 807 842	9.3	8.0
√	×	√	×	0.837	0.554	2 339 190	6.3	4.8
√	×	×	√	0.834	0.568	3 154 274	8.1	6.5
√	√	√	√	0.847	0.571	3 287 670	7.5	6.7

模型	P/%	R/%	mAP@50/ %	FLOPs/ 10⁹	Params/ 10⁶	Size/ MB
SSD	61.7	78.0	62.5	30.7	24.4	187.6
Faster R-CNN	61.8	77.1	62.6	208.0	41.4	317.5
YOLOXs	63.4	79.6	64.9	7.6	5.0	70.4
RTMDET-tiny	65.1	80.0	65.7	30.7	24.4	31.7
Cascade RCNN	69.3	75.3	69.7	236.0	69.2	533.0
DINO R-50	65.9	75.2	68.9	274.0	47.6	602.9
YOLOV5s	77.3	76.1	79.8	4.2	1.8	3.8
YOLOV7-tiny	79.6	76.7	80.6	13.1	6.0	11.7
YOLOV10n	83.0	74.0	80.9	6.5	2.3	5.7
YOLO11n	80.8	77.3	81.8	6.3	2.6	5.5
YOLOV12n	82.3	78.0	83.2	5.8	2.5	5.2
YOLOV13n	82.6	73.0	80.9	6.2	2.5	5.2
Hyper-YOLO	82.8	76.1	82.3	11.0	3.9	7.8
YOLOV8n	81.3	75.4	82.1	8.1	3.0	6.2
本文模型	83.3	79.3	84.7	7.5	3.3	6.7

Maritime ship detection algorithm under complex weather environments based on enhanced YOLOv8

基于改进YOLOv8的复杂天气环境下海面船舶检测算法

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References 29

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