基于Involution Prediction Head的小目标检测算法  

Small object detection algorithm based on involution prediction head

作　　者：安鹤男[1] 邓武才 管聪 姜邦彦 An Henan;Deng Wucai;Guan Cong;Jiang Bangyan(College of Electronics and Information Engineering,Shenzhen University,Shenzhen 518000,China;Institute of Microscale Optoelectronics,Shenzhen University,Shenzhen 518000,China)

机构地区：[1]深圳大学电子与信息工程学院,广东深圳518000 [2]深圳大学微纳光电子学研究院,广东深圳518000

出　　处：《电子技术应用》2022年第11期19-23,共5页Application of Electronic Technique

摘　　要：针对通用目标检测算法在检测小目标时存在错检和漏检等问题,提出了一种小目标检测算法IPH(Involution Prediction Head),将其运用在YOLOv4和YOLOv5的检测头部分,在VOC2007数据集上的实验结果表明,运用IPH后的YOLOv4小目标检测精度APs(AP for small objects)相比原始算法提升了1.1%,在YOLOv5上的APs更是提升了5.9%。经智能交通检测数据集进一步检验,IPH算法和去下采样能有效提升小目标检测精度,减少错检和漏检的情况。Aiming at the problems of false positive detection and low recall in the detection of small targets by the general target detection algorithm,a small target detection algorithm IPH(involution prediction head)is proposed,which is applied to the detection head of YOLOv4 and YOLOv5.The experimental results on the VOC2007 data set show that the detection accuracy APs(AP for small objects)of YOLOv4 after using IPH is improved by 1.1% compared with the original algorithm,and the APs on YOLOv5 is improved by 5.9%.Through further verification of the intelligent traffic detection data set,IPH algorithm and desampling can effectively improve the accuracy of small object detection and reduce false positive detection and missed detection.

关 键 词：YOLOv4 IPH 小目标检测 特征提取 注意力机制 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]