SSD模型在门式起重机障碍物检测中的应用
胡晓兵1，杨雄1，卢斯伟2，何政霖2，郭磊3
（1.四川大学 制造科学与工程学院，四川 成都 610065；2.中国人民解放军第5719厂，四川 彭州 611937；3.浙江亿利达风机股份有限公司，浙江 台州 318056）
摘要：针对传统的门式起重机障碍物检测方式与避障手段中易受自然环境、现场条件、后期维护等因素的影响以及功能泛化能力较差的问题，提出了一种基于视觉的SSD模型障碍物检测方法。这种检测方式是一种基于回归方法的深度学习目标检测算法，通过对输入图像进行卷积和池化处理等操作提取特征向量，大大提高了对图片中特征检测准确率。采用VOC数据集中的行人、狗、猫、水杯、自行车图片集加上无障碍轨道图片作为训练集，并且训练过程中结合多尺度图像和多环境背景图像来降低复杂环境对检测的影响。实验结果表明，所提供的方法能够有效地提取本文规定的特征，解决了传统门式起重机障碍物检测方式与避障手段的不足，同时提高了运行过程中的安全性。
关键词：门式起重机；障碍物检测；深度学习；SSD模型
中图分类号：TP391 文献标志码：A doi：10.3969/j.issn.1006-0316.2019.02.012
文章编号：1006－0316 (2019) 02－0056－07
SSD Model in Obstacle Detection of Door Crane
HU Xiaobing1，YANG Xiong1，LU Siwei2，HE Zhenglin2，GUO Lei3
( 1.School of Manufacturing Science and Engineering, Sichuan University, Chengdu 610065, China;2.Chinese People's Liberation Army 5719th Factory, Pengzhou 611937, China; 3.Zhejiang Yilida Ventilator Co., Ltd., Taizhou 318056, China )
Abstract：The traditional gantry crane obstacle detection methods are vulnerable to the influence of natural environment, field condition, the maintenance and so on, and its function generalization ability is poor. In order to solve the problems, this paper proposed a visual obstacle detection method based on SSD model. This detection method is a kind of deep learning detection algorithm based on regression method. It uses convolution and pooling to process the input image, and extract the feature vectors, which greatly improves the accuracy of feature detection in the images. In this paper, we use pedestrians, dogs, cats, water cups and bicycles in the VOC data set as a training set. Meanwhile a free obstacle track picture is added as a training set. In addition, we use combining multi-scale images and multi environment background images in the training process to reduce the impact of complex environment on detection. The experimental results show that the proposed method can effectively extract the features specified in this paper and overcome the disadvantages of the traditional gantry crane obstacle detection and obstacle avoidance, while the security of the operation process is improved.
Key words：door crane；obstacle detection；deep learning；SSD model
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收稿日期：2018－06－21
基金项目：四川省科技计划项目（2016KJ0059-2015GZ0014）；四川省科技计划项目（2016KJT0082-2016GZ0162）
作者简介：胡晓兵（1970－），男，湖北黄冈人，博士，教授，主要研究方向为数字化车间；杨雄（1992－），男，重庆人，硕士研究生，主要研究方向为智能制造。
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