摘要
针对加油站场景中的目标检测算法存在检测精度低的问题,提出一种基于Yolov3-Tiny的加油站场景目标检测改进算法。该算法以Yolov3-Tiny模型为基础网络,引入Yolov4算法提出的Mosaic图像增强方式进行数据预处理,采用密集连接模块重构特征提取网络,并将CBAM(Convolutional Block Attention Module)注意力模块与金字塔池化模块(Pyramid Pooling Module)加入到网络中,最终实现了加油站场景下的目标检测。实验结果表明,改进的算法相比于原算法的总体mAP提升了8.2%,能更有效地应用于加油站目标检测中。
We present an improved target detection algorithm based on Yolov3-Tiny for gas station scene because of the low accuracy of target detection algorithm in gas station scenes.This algorithm takes Yolov3-Tiny model as the basic network,innovates Mosaic image enhancement method proposed in Yolov4 algorithm for data preprocessing,uses dense connection modules to reconstruct the feature extraction network,and adds CBAM(Convolutional Block Attention Module)attention mechanism and Pyramid Pooling Module into the network,finally target detection in the gas station scene is realized.The experimental results show that the improved algorithm improves the overall mAP by 8.2%compared with the original algorithm,and can be more effectively applied to gas station target detection.
作者
张利巍
杨万帅
ZHANG Liwei;YANG Wanshuai(School of Physics and Electronic Engineering,Northeast Petroleum University,Daqing 163318,China)
出处
《吉林大学学报（信息科学版）》
CAS
2024年第3期559-566,共8页
Journal of Jilin University（Information Science Edition）
关键词
目标检测
密集连接模块
注意力机制
金字塔池化模块
图像增强
target detection
dense connection module
attention mechanism
pyramid pool module
image enhancement
