首页|基于SOE-YOLO轻量化的水面目标检测算法

基于SOE-YOLO轻量化的水面目标检测算法

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针对复杂多变的水面环境,小目标检测存在漏检、错检且检测平台计算资源有限的问题,提出了基于YOLOv8 的轻量化水面目标检测算法SOE-YOLO.首先在Neck部分使用包含GSConv的Slim-Neck设计范式对模型进行轻量化改进;其次通过使用轻量型卷积(ODConv)模块重新构建Backbone部分,以减少参数量从而提高网络的检测速度;最后引入多尺度注意力机制(EMA)增强网络多尺度特征提取能力,提高小目标检测能力.在WSODD测试集中的实验结果表明,SOE-YOLO模型参数量和计算量分别为 2.8 M和 6.6 GFLOPs,与原模型相比分别减少 12.5%和 18.6%,同时mAP@%0.5 和mAP@0.5-0.95 分别达到 79.9%和 47.2%,与原模型相比分别提高 2.4%和 1.6%,且漏检率下降明显,优于当前流行的目标检测算法.FPS达到了 64.25,满足水面目标检测实时性的要求.在实现轻量化的同时具有更好的检测性能,满足了在计算资源受限环境下的部署需求.
A water surface target detection algorithm based on SOE-YOLO lightweight network
A lightweight water surface object detection algorithm SOE-YOLO based on YOLOv8 was proposed to address the issues of missed and false detections in complex and ever-changing water surface environments,as well as limited computing resources on the detection platform.Firstly,the Slim-Neck paradigm containing GSConv was employed to improve the weight of the model in the Neck part.Secondly,the Backbone section was reconstructed using a lightweight convolutional ODConv(omni-dimensional dynamic convolution)module,thereby reducing the number of parameters to improve the detection speed of the network.Finally,the multi-scale attention mechanism EMA(effective multi-scale attention)was introduced to enhance the network's capability in extracting multi-scale features,thereby enhancing the small target detection accuracy.The experimental results on the WSODD(water surface object detection)test set demonstrated that the parameter and computational quantities of the SOE-YOLO model were 2.8 M and 6.6 GFLOPs,respectively,which were reduced by 12.5%and 18.6%compared to the original model.At the same time,mAP@%0.5 and mAP@0.5-.95 reached 79.9%and 47.2%,respectively,which were 2.4%and 1.6%higher than the original model,and the missed detection rate decreased significantly,outperforming the current popular object detection algorithms.The FPS reached 64.25,meeting the requirements of real-time detection of surface targets.It could achieve better detection performance,while achieving lightweight,meeting deployment requirements in computing-resource-constrained environments.

water surface object detectionYOLOV8lightweight improvementSlim-Neck design paradigmattention mechanisms

曾志超、徐玥、王景玉、叶元龙、黄志开、王欢

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南昌工程学院信息工程学院,江西 南昌 330000

南昌工程学院机械工程学院,江西 南昌 330000

水面目标检测 YOLOv8 轻量化改进 Slim-Neck设计范式 注意力机制

国家重点研发计划项目国家自然科学基金项目江西省研究生创新专项江西省研究生创新专项

2019YFB170450261472173yc2023-s995YJSCX202312

2024

10.11996/JG.j.2095-302X.2024040736

图学学报
中国图学学会

图学学报

CSTPCD北大核心
影响因子：0.73
ISSN：2095-302X
年,卷(期)：2024.45(4)