基于TLF-YOLOv8的堆叠垃圾实例分割算法研究
DOI:
作者:
作者单位:

西安科技大学 电气与控制工程学院

作者简介:

通讯作者:

中图分类号:

TP 391.4

基金项目:

国家自然科学基金(61603295);陕西省自然科学基础研究计划(No.2024JC-YBQN-0726);陕西省自然科学基础研究计划(2024JC-YBQN-0726);陕西省教育厅科研计划项目资助(23JK0550);西安市科技计划(23DCYJSGG0025-2022)


Stacked Garbage Instance Segmentation Based on Double-Layer DCT-Mask Feature Fusion Algorithm
Author:
Affiliation:

1.Xi '2.'3.an University of Science and Technology, School of Electrical and Control Engineering

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    相较于一般场景下的图像实例分割,复杂堆叠场景下的实例分割受到严重遮挡、同类别待测物体堆叠等复杂情况的影响,使得其实例分割具有更大的难度。本文针对具有复杂堆叠场景下的垃圾实例分割问题,提出了一种融合YOLOv8与双层特征网络策略的实例分割算法。首先,在数据预处理部分进行特征数据分层,并通过双层图卷积网络(Graph Convolutions Network,GCN)实现双分支特征融合,减弱堆叠情况对被遮挡物体特征的影响,从而解决复杂堆叠遮挡下的实例分割问题。同时,为了解决同类待测物体易混淆的问题,融入了软阈值化非极大值抑制算法和新的交并比算法。最后,根据应用场景和数据集的复杂性,优化了主干网络部分的特征提取模块,并在主干网络部分引入了多尺度注意力机制,有效提高了模型的检测性能。本次实验使用遮挡垃圾分类实例分割数据集,实验结果表明该方法的平均准确率、交并比阈值为0.5时的平均准确率(Average Precision50,AP50)、AP50?95等指标较之前的其它方法更优。相较于原YOLOv8算法,检测AP50提高了7.9%,分割AP50提高了5.4%,具有更好的检测和分割效果。

    Abstract:

    Compared to image instance segmentation in general scenes, instance segmentation in complex stacked scenes is affected by complex situations such as severe occlusion and stacking of similar objects, making instance segmentation more difficult. This article proposes an instance segmentation algorithm that combines YOLOv8 with a dual layer feature network strategy for garbage instance segmentation in complex stacking scenarios. Firstly, in the data preprocessing section, feature data is layered and dual branch feature fusion is achieved through a dual layer Graph Convolutions Network (GCN) to reduce the impact of stacking on the features of occluded objects, thus solving the problem of practical segmentation under complex stacking occlusion. At the same time, in order to solve the problem of confusion among similar test objects, soft thresholding non maximum suppression algorithms and new intersection and union ratio algorithms have been incorporated. Finally, based on the complexity of the application scenario and dataset, the feature extraction module of the backbone network was optimized, and a multiscale attention mechanism was introduced in the backbone network, effectively improving the detection performance of the model. This experiment used occluded garbage classification instances to segment the dataset, and the experimental results showed that the average accuracy, average precision 50 (AP50), and AP50-95 of this method were better than other previous methods when the intersection to union ratio threshold was 0.5. Compared to the original YOLOv8 algorithm, the detection AP50 has increased by 7.9% and the segmentation AP50 has increased by 5.4%, resulting in better detection and segmentation performance.

    参考文献
    相似文献
    引证文献
引用本文

李利,梁晶,陈旭东,等. 基于TLF-YOLOv8的堆叠垃圾实例分割算法研究[J]. 科学技术与工程, , ():

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2024-03-31
  • 最后修改日期:2024-05-31
  • 录用日期:2024-06-05
  • 在线发布日期:
  • 出版日期:
×
亟待确认版面费归属稿件,敬请作者关注