LESO-Net:一种轻量高效的小目标分割网络
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作者单位:

南京市南京信息工程大学

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


LESO-Net A Lightweight and Efficient Segmentation Network for Small Object
Author:
Affiliation:

Nanjing University of Information Science and Technology

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    图像内的部分小目标因其具有形状不规则以及边界模糊等特征,在分割处理时常常遭遇诸多挑战,主要包括特征提取困难、边缘细节丢失、噪声干扰显著等。针对以上问题,本文提出一种基于YOLOv8n-seg模型的轻量高效的小目标分割网络LESO-Net。首先,使用可变形卷积网络(DCNv2)替换骨干网络中的C2f模块,以提高对不同形状小目标的特征提取和自适应泛化能力;然后,将大可分离核注意力(LSKA)模块引入到颈部网络中,以提高分割精确度,降低计算复杂度和内存占用;最后,通过对损失函数进行优化,改善类别不平衡和边界框精确度不足的问题。在自建的气泡数据集和SAR公共图像数据集(HRSID)上进行的实验结果表明,改进后的网络LESO-Net与原始YOLOv8n-seg模型相比,精确度分别提高1.2和2.5个百分点,mAP0.5分别提高0.2和1.2个百分点,参数量减少10%,证明所提出的LESO-Net模型具有较好的综合性能,能够满足复杂场景中小目标分割任务的要求。

    Abstract:

    Small objects in images often present significant challenges during segmentation due to their irregular shapes and blurred boundaries. These challenges primarily include difficulties in feature extraction, loss of edge details, and significant noise interference. To effectively address these challenges, we propose an efficient small object segmentation model named LESO-Net, a lightweight and efficient object segmentation Network for small objects, based on You Only Look Once (YOLO) v8n-seg. Initially, we integrate a Large Separable Convolution Attention (LSKA) module into the Neck network, which not only enhances segmentation accuracy but also reduces computational complexity and memory usage. In addition, to specifically address the unstable shapes of small objects, we replace the C2f module in the backbone model with our improved Deformable Convolutional Networks (DCNv2). This modification significantly enhances feature extraction and adaptive generalization capabilities for small objects of varying shapes. Furthermore, to further improve the model's performance, we ameliorate the loss function, thereby effectively tackling class imbalances and insufficient bounding box accuracy. We validated the effectiveness of LESO-Net on a self-constructed bubble dataset and a public high-resolution SAR images dataset (HRSID). Compared to the original version of YOLOv8n-seg, LESO-Net achieved a precision improvement of 1.2 percentage points and 2.5 percentage points, along with average precision enhancements of 0.2 percentage point and 1.2 percentage points, respectively, while the number of parameters was reduced by nearly 10%. This result effectively demonstrates the superior performance of the LESO-Net model, meeting the accuracy requirements for segmenting small targets in practical remote sensing scenarios.

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丁正龙,胡一凡,杜元洪,徐炜杰,魏哑美,姚选. LESO-Net:一种轻量高效的小目标分割网络[J].南京信息工程大学学报,,():

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历史
  • 收稿日期:2024-12-28
  • 最后修改日期:2025-02-11
  • 录用日期:2025-02-13

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