|Table of Contents|

Research on Warehouse Object Detection AlgorithmBased on Convolutional Neural Network(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2019年04期
Page:
99-105
Research Field:
计算机工程
Publishing date:

Info

Title:
Research on Warehouse Object Detection AlgorithmBased on Convolutional Neural Network
Author(s):
Wang Fei1; Chen Liangjie2; Wang Li2; Wang Lin2
(1.College of Humanities & Sciences of Guizhou Minzu University,Guiyang 550025,China)(2.College of Data Science and Information Engineering,Guizhou Minzu University,Guiyang 550025,China)
Keywords:
convolutional neural network; warehouse environment; object detection; deeply supervised object detectors(DSOD)
PACS:
TP391.41
DOI:
10.3969/j.issn.1672-1292.2019.04.017
Abstract:
Considering the lack of public datasets for object detection based on the warehouse environment,a large number of images containing cargos,trays and forklifts in real warehouse environment are collected and labeled to build the warehouse object dataset. Meanwhile,aiming at the problem that the traditional object detection algorithm has lower detection accuracy in warehouse environment,the deeply supervised object detectors(DSOD)based on convolutional neural network is applied to the warehouse environment,and the DSOD model is trained from scratch on the self-built warehouse object dataset,and the accuracy detection of the warehouse object is realized. The mean Average Precision(mAP)of this algorithm reaches 93.81%,which is higher than that of Faster R-CNN and SSD by 0.04 and 1.44 points respectively,and the model size of this algorithm is only 51.3 MB,which is lower than that of Faster R-CNN and SSD by 184.5 MB and 43.4 MB respectively. The experimental results show that the algorithm has a relatively satisfying warehouse object detection effect,and it has certain practical values in the field of warehouse object detection.

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Last Update: 2019-12-31