陈永刚,陈丽珊,邹易,孙余顺.基于深度学习的包装组件检测系统研究[J].包装工程,2021,42(15):284-291.
CHEN Yong-gang,CHEN Li-shan,ZOU Yi,SUN Yu-shun.Packaging Component Detection System Based on Deep Learning[J].Packaging Engineering,2021,42(15):284-291. |
| 基于深度学习的包装组件检测系统研究 |
| Packaging Component Detection System Based on Deep Learning |
| 投稿时间：2020-05-24 |
| DOI：10.19554/j.cnki.1001-3563.2021.15.037 |
| 中文关键词: 包装组件 深度学习 目标检测 |
| 英文关键词: package components deep learning object detection |
| 基金项目:广东省省级重大科技计划(2017B090910012);广东省教育厅2018年度普通高校特色创新项目(自然科学)(2018GKTSCX106);2020年广东省科技创新战略专项资金(pdjh2020b1264) |
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| 中文摘要: |
| 目的 针对人工分拣组成的零件包装盒常常会出现缺少部分零件的问题，开发一套集训练、识别、分选于一体的智能分拣系统。方法 在设计过程中，提出一种基于深度学习的改进Yolov3算法，针对工业现场光照、业零件形状和质地等实际因素，对Yolo算法的训练和检测进行改进，通过对包装盒产品的一次拍摄，检测出画面中出现的预设物体，并与标准设置相比对，从而判断出该盒内产品是否有缺料、多料的情况，以此分选出合格与否的包装盒。结果 在物体摆放相互重叠不超过20%的情况下，物体检测的准确率为98.2%，召回率为99.5%。结论 通过文中提出的改进算法，设计的检测系统能够在复杂的工业现场环境下正常工作，并能对包装的完整性进行准确的检测。 |
| 英文摘要: |
| Aiming at the problem that parts packaging boxes composed of manual sorting often have missing parts, an intelligent sorting system integrating training, recognition and sorting is developed. The system can detect the preset objects appearing on the screen through one shot of the product in the box, and compare it with the standard settings to determine whether the product in the box is short of material or has too much material, so that qualified and unqualified boxes can be selected. In the design process, an improved Yolov3 algorithm based on deep learning was proposed. In view of practical factors such as industrial site lighting, industrial part shape and texture, the training and detection of the Yolo algorithm were improved. In the case where the objects placed overlap each other within 20%, the accuracy rate of object detection is 98.2%, and the recall rate is 99.5%. Through the improved algorithm proposed in this paper, the designed detection system can work normally in a complex industrial field environment, and can accurately detect the integrity of the packaging. |
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