[1]施泽浩,赵启军.基于全卷积网络的目标检测算法[J].计算机技术与发展,2018,28(05):55-58.[doi:10.3969/j.issn.1673－629X.2018.05.013]
　SHI Ze-hao,ZHAO Qi-jun.Object Detection Algorithm Based on Fully Convolutional Neural Network[J].,2018,28(05):55-58.[doi:10.3969/j.issn.1673－629X.2018.05.013]
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基于全卷积网络的目标检测算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年05期
页码:
55-58
栏目:
智能、算法、系统工程
出版日期:
2018-05-10

文章信息/Info

Title:
Object Detection Algorithm Based on Fully Convolutional Neural Network
文章编号:
1673-629X(2018)05-0055-04
作者:
施泽浩; 赵启军
四川大学 计算机学院 视觉合成图形图像技术国防重点实验室，四川 成都 610065
Author(s):
SHI Ze-hao; ZHAO Qi-jun
National Key Laboratory of Fundamental Science on Synthetic Vision，School of Computer Science，Sichuan University，Chengdu 610065，China
关键词:
目标检测; 深度学习; 全卷积神经网络; 回归; 计算机视觉
Keywords:
object detection; deep learning; convolutional neural network; regression; computer vision
分类号:
TP301.6
DOI:
10.3969/j.issn.1673－629X.2018.05.013
文献标志码:
A
摘要:
目标检测是计算机视觉的一项重要任务，其主要内容是定位图像中出现的目标，并对其进行分类。主流算法普遍基于卷积加全连接的结构，存在模型参数巨大、检测效率低下等问题。而在现实应用中，比如自动驾驶车载系统、智能监控系统中对行人、车辆等目标的检测，往往对目标检测算法的实时性具有较高要求。为此，提出一种基于全卷积神经网络的目标检测算法。网络结构完全采用卷积层实现，不仅用卷积进行特征提取，而且用卷积层进行预测，采用多任务学习，大大提高了检测效率并降低了模型复杂度。相比主流深度学习目标检测算法，如 YOLO、Faster RCNN，该算法速度更快，模型参数更少，且保持相当的精度，在 PASCAL VOC2007 权威目标检测库上的平均准确率(mAP)达到 64．5。
Abstract:
 Object detection is a primary mission in computer vision that focuses on localization and classification of objects in an image．Almost all the mainstream algorithms use convolution structure followed by fully connected layer，which lead to huge number of model parameters and poor efficiency．In real application like automatic driving，intelligence CCTV and so on，high inference efficiency is required．For this，we propose a fully convolutional based object detection algorithm．The network structure is implemented by convolution layer，both feature extraction and prediction through convolution．Multi－task learning is utilized to improve detection efficiency greatly and reduce the complexity of model．Compared to YOLO and Faster ＲCNN，the proposed algorithm is faster，less in model parameters and maintain its precision．The average accuracy on PASCAL VOC2007 dataset reaches to 64.5．

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更新日期/Last Update: 2018-06-28