Misp-YOLO:加油站场景目标检测  

Misp⁃YOLO: Gas Station Scene Target Detection

作　　者：刘远红[1] 程明皓 LIU Yuanhong;CHENG Minghao(School of Electrical Information and Engineering,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学电气信息工程学院,黑龙江大庆163318

出　　处：《吉林大学学报（信息科学版）》2024年第1期168-175,共8页Journal of Jilin University（Information Science Edition）

摘　　要：针对Yolov3-Tiny算法在加油站监控场景检测时由于数据特征提取不充分而导致检测精度低、漏检率高等问题,提出一种基于加油站场景的Misp-YOLO(You Only Look Once)目标检测算法。首先引入Mosaic数据增强算法,使图片包含更多特征信息;其次使用InceptionV2和PSConv(Poly-Scale Convolution)多尺度特征提取方法提升网络多尺度预测能力;最后结合scSE(Concurrent Spatial and Channel ‘Squeeze&Excitation’)注意力机制,重构主干网络输出特征。实验结果证明该算法具有较高检测准确度,并且检测速度满足实际需求。优化后的算法性能得到极大提升,可推广应用于其他目标检测中。In order to solve the problem that Yolov3⁃Tiny algorithm has insufficient feature extraction in gas station monitoring scene detection,which results in low detection accuracy,a new target detection algorithm based on gas station scene is proposed.This method first introduces Mosaic data enhancement algorithm to make the picture contain more feature information.Secondly,InceptionV2 and PSConv(Poly⁃Scale Convolution)multiscale feature extraction methods are used to improve the network multiscale prediction ability.Finally,combined with the scSE(Concurrent Spatial and Channel‘Squeeze&Excitation’)attention mechanism,the output characteristics of the backbone network are reconstructed.The experimental results show that the algorithm has high detection accuracy and the detection speed meets the actual needs.The performance of the optimized algorithm is greatly improved and can it be applied to other target detection.

关 键 词：目标检测 YOLO算法 特征提取 注意力机制 多尺度预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]