基于改进卷积神经网络的小目标检测算法

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万方数据
维普

中文摘要：对于在检测时存在小尺度检测目标漏检、不精确等问题，提出一种改进的YOLO v4算法模型(F-YOLO v4)。利用改进的K均值聚类算法对数据集进行聚类，使得锚点框的大小更适用于目标检测;采用深度卷积和逐点卷积相结合的方法对通道内和通道间的卷积进行分离，从而改善了原有的残差块;采用通道注意力机制对骨干网络进行改进的同时在PANet网络中添加RFB模块，增强特征提取能力，从而提高了对小目标的检测效果。实验结果表明，F-YOLO v4算法在KITTI数据集上平均精度均值达到了93。67％，与原算法对比提高了1。52百分点，并且比较目前其他主流网络有着较高的精确度。

外文标题：SMALL TARGET DETECTION ALGORITHM BASED ON IMPROVED CONVOLUTIONAL NEURAL NETWORK

外文摘要：In order to solve the problems of small-scale target missing detection and imprecision in detection,an improved YOLO v4 algorithm model(F-YOLO v4)is proposed.The improved K-means clustering algorithm was used to cluster the data set,which made the size of anchor frame more suitable for target detection.The convolution within and between channels was separated by the combination of deep convolution and point by point convolution,so as to improve the original residual block.The channel attention mechanism was used to improve the backbone network,and the RFB module was added to the PANet network to increase the number of residual blocks.The feature extraction ability was enhanced,and the detection effect of small target was improved.The experimental results show that the average accuracy of F-YOLO v4 algorithm on KITTI data set reaches 93.67％,which is 1.52 percentage points higher than the original algorithm,and has higher accuracy than other mainstream networks.

外文关键词：

Improved YOLO v4 algorithmDepth separable convolutionAttention mechanismRFB moduleSmall target detection

作者：

张明、余志强

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作者单位：

北京全路通信信号研究设计院集团有限公司北京 100070

石家庄铁道大学电气与电子工程学院河北石家庄 050043

石家庄铁道大学河北省电磁环境效应与信息处理重点实验室河北石家庄 050043

关键词：

改进的YOLO v4算法深度可分离卷积注意力机制 RFB模块小目标检测

基金：

国家自然科学基金项目河北省自然科学基金面上项目河北省重点研发计划项目石家庄市军民融合项目国家轨道交通电磁环境效应研究与测试平台建设项目

项目编号：

11872257E201821014420354501D201060104A50200011800

出版年：

2024

DOI：

10.3969/j.issn.1000-386x.2024.09.024

计算机应用与软件

上海市计算技术研究所上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心

影响因子：0.615

ISSN：1000-386X

年,卷(期)：2024.41(9)