Intelligent Detection of Dangerous Goods in Security Inspection Based on Cascade Cross Stage YOLOv3 Model
At present, it mainly depends on the human eye to identify the X-ray scanning image, when the security detector is used to detect the dangerous goods in the baggage. It is labor intensive and prone to false or missed detection. This paper proposes an intelligent detection method of dangerous goods i...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2022-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/398881 |
| _version_ | 1797206703537651712 |
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| author | Jianjun Wu Shaowen Liao |
| author_facet | Jianjun Wu Shaowen Liao |
| author_sort | Jianjun Wu |
| collection | DOAJ |
| description | At present, it mainly depends on the human eye to identify the X-ray scanning image, when the security detector is used to detect the dangerous goods in the baggage. It is labor intensive and prone to false or missed detection. This paper proposes an intelligent detection method of dangerous goods in security inspection based on a novel cascaded cross-stage YOLOv3 model (abbreviated to CCS-YOLOv3). Considering the different sizes, disorderly lay or serious overlap of various objects in the scanning image, this method first enhances the scanned image to improve the quality of the data set. After that, the traditional YOLOv3 is improved by cascading cross-stage mode, and the backbone network of YOLOv3 is improved to cascade cross-stage Darknet. And then the backbone network is followed by a spatial pyramid pooling (SPP) module. Following that, the feature pyramid network (FPN) is connected in series with a bottom-up feature pyramid structure to realize the feature fusion. The results of model Ablation experiment and baggage scanning image detection show that the cascade cross-stage YOLOv3 model significantly improves the image detection speed and precision, and the model is effective and feasible. |
| first_indexed | 2024-04-24T09:11:14Z |
| format | Article |
| id | doaj.art-21e01fe68d3149e0972a779979c44c84 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:11:14Z |
| publishDate | 2022-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-21e01fe68d3149e0972a779979c44c842024-04-15T17:39:17ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392022-01-0129388889510.17559/TV-20220222034206Intelligent Detection of Dangerous Goods in Security Inspection Based on Cascade Cross Stage YOLOv3 ModelJianjun Wu0Shaowen Liao1College of Information Technology and Communication, Hexi University, No. 846, Huancheng North Road, Zhangye, Gansu Province, ChinaCollege of Information Technology and Communication, Hexi University, No. 846, Huancheng North Road, Zhangye, Gansu Province, ChinaAt present, it mainly depends on the human eye to identify the X-ray scanning image, when the security detector is used to detect the dangerous goods in the baggage. It is labor intensive and prone to false or missed detection. This paper proposes an intelligent detection method of dangerous goods in security inspection based on a novel cascaded cross-stage YOLOv3 model (abbreviated to CCS-YOLOv3). Considering the different sizes, disorderly lay or serious overlap of various objects in the scanning image, this method first enhances the scanned image to improve the quality of the data set. After that, the traditional YOLOv3 is improved by cascading cross-stage mode, and the backbone network of YOLOv3 is improved to cascade cross-stage Darknet. And then the backbone network is followed by a spatial pyramid pooling (SPP) module. Following that, the feature pyramid network (FPN) is connected in series with a bottom-up feature pyramid structure to realize the feature fusion. The results of model Ablation experiment and baggage scanning image detection show that the cascade cross-stage YOLOv3 model significantly improves the image detection speed and precision, and the model is effective and feasible.https://hrcak.srce.hr/file/398881cascade cross stage networksdetection of dangerous goodsfeature fusionintelligent security inspectionYOLOv3 model |
| spellingShingle | Jianjun Wu Shaowen Liao Intelligent Detection of Dangerous Goods in Security Inspection Based on Cascade Cross Stage YOLOv3 Model Tehnički Vjesnik cascade cross stage networks detection of dangerous goods feature fusion intelligent security inspection YOLOv3 model |
| title | Intelligent Detection of Dangerous Goods in Security Inspection Based on Cascade Cross Stage YOLOv3 Model |
| title_full | Intelligent Detection of Dangerous Goods in Security Inspection Based on Cascade Cross Stage YOLOv3 Model |
| title_fullStr | Intelligent Detection of Dangerous Goods in Security Inspection Based on Cascade Cross Stage YOLOv3 Model |
| title_full_unstemmed | Intelligent Detection of Dangerous Goods in Security Inspection Based on Cascade Cross Stage YOLOv3 Model |
| title_short | Intelligent Detection of Dangerous Goods in Security Inspection Based on Cascade Cross Stage YOLOv3 Model |
| title_sort | intelligent detection of dangerous goods in security inspection based on cascade cross stage yolov3 model |
| topic | cascade cross stage networks detection of dangerous goods feature fusion intelligent security inspection YOLOv3 model |
| url | https://hrcak.srce.hr/file/398881 |
| work_keys_str_mv | AT jianjunwu intelligentdetectionofdangerousgoodsinsecurityinspectionbasedoncascadecrossstageyolov3model AT shaowenliao intelligentdetectionofdangerousgoodsinsecurityinspectionbasedoncascadecrossstageyolov3model |