Tiny YOLOV3目标检测改进  

Improvement of Tiny YOLOV3 target detection

作　　者：马立[1] 巩笑天 欧阳航空[1] MA Li;GONG Xiao-tian;OUYANG Hang-kong(School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学机电工程与自动化学院,上海200444

出　　处：《光学精密工程》2020年第4期988-995,共8页Optics and Precision Engineering

基　　金：国家自然科学基金资助项目(No.61573238);国家重点研发计划资助项目(No.2018YFB1309200)。

摘　　要：针对Tiny YOLOV3目标检测算法在实时检测中对行人等小目标漏检率高的问题,对该算法的特征提取网络、预测网络、损失函数等进行研究改进。首先,在特征提取网络中增加2步长的卷积层,代替原网络中的最大池化层进行下采样;接着,使用深度可分离卷积构造反残差块替换传统卷积,降低模型尺寸和参数量,增加高维特征提取;然后,在原网络两尺度预测的基础上增加一尺度,形成三尺度预测;最后,对损失函数中的边界框位置误差项进行优化。实验结果表明,改进后的Tiny YOLOV3算法的目标检测准确率比原算法提高了9.8%,满足实时性要求,具有一定鲁棒性。本文方法能够更好地提取目标特征,多尺度预测和边界框位置误差的改进能更准确地对目标进行检测。The Tiny YOLOV3 target detection algorithm has a high error rate for small targets,such as pedestrians,in real-time detection.Therefore,this study aimed to improve the feature extraction network,prediction network,and loss function of the algorithm.First,a two-step convolution layer was added to the feature extraction network to replace the maximum pooling layer in the original network for downsampling.Second,the traditional convolution was replaced with an anti-residual block constructed by a deep convolutional convolution to reduce the model size as well as number of parameters and increase the high-dimensional feature extraction.Third,based on the original two-scale prediction of the network,a scale was added to form a three-scale prediction.Finally,the boundary box position error in the loss function was optimized.The experimental results demonstrat that the improved Tiny YOLOV3 algorithm achieve a target detection accuracy that IS 9.8%higher than the original algorithm,satisfied the real-time requirement,and demonstratedrobustness.The proposed method can better extract target features,and the multi-scale prediction and improvement of the boundary box position error can detect targets more accurately.

关 键 词：目标检测 TINY YOLOV3 深度可分离卷积 反残差块 多尺度预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]