A bullet recognition algorithm based on improved YOLOX
The identification and positioning of flying projectiles is a critical issue that affects the testing accuracy of equipment in ballistic testing technology. Traditional image processing methods are difficult to accurately extract targets due to the characteristics of small target size, fast speed, a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2023.1213489/full |
| _version_ | 1827908771998859264 |
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| author | Jie Hu Jie Hu Hua Chen Yubo Xu Yu Fu |
| author_facet | Jie Hu Jie Hu Hua Chen Yubo Xu Yu Fu |
| author_sort | Jie Hu |
| collection | DOAJ |
| description | The identification and positioning of flying projectiles is a critical issue that affects the testing accuracy of equipment in ballistic testing technology. Traditional image processing methods are difficult to accurately extract targets due to the characteristics of small target size, fast speed, and strong fragmentation interference of projectiles ejected from the muzzle. This paper proposes a projectile recognition algorithm based on an improved YOLOX detection model for the detection and recognition of flying projectiles. The fast and accurate YOLOX model is used, and the network structure is improved by adding attention mechanisms in the feature fusion module to enhance the detection ability of small targets; the loss function is also improved to enhance the model’s iterative optimization ability. Test results show that the improved YOLOX model has significantly improved accuracy in projectile recognition compared to the original network, reaching 84.82%, demonstrating the feasibility of the proposed approach. The improved algorithm can be effectively used for small target scenarios in range testing and significantly improves the accuracy of recognition. |
| first_indexed | 2024-03-13T01:27:00Z |
| format | Article |
| id | doaj.art-42c2e355fe6f4097b2c1db7a1190bf52 |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-03-13T01:27:00Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-42c2e355fe6f4097b2c1db7a1190bf522023-07-04T12:48:52ZengFrontiers Media S.A.Frontiers in Physics2296-424X2023-07-011110.3389/fphy.2023.12134891213489A bullet recognition algorithm based on improved YOLOXJie Hu0Jie Hu1Hua Chen2Yubo Xu3Yu Fu4School of Mechatronic Engineering, Xi’an Technological University, Xi’an, ChinaNorinco Group Testing and Research Institute, Huayin, ChinaSchool of Mechatronic Engineering, Xi’an Technological University, Xi’an, ChinaSchool of Optoelectronic Engineering, Xi’an Technological University, Xi’an, ChinaNorinco Group Testing and Research Institute, Huayin, ChinaThe identification and positioning of flying projectiles is a critical issue that affects the testing accuracy of equipment in ballistic testing technology. Traditional image processing methods are difficult to accurately extract targets due to the characteristics of small target size, fast speed, and strong fragmentation interference of projectiles ejected from the muzzle. This paper proposes a projectile recognition algorithm based on an improved YOLOX detection model for the detection and recognition of flying projectiles. The fast and accurate YOLOX model is used, and the network structure is improved by adding attention mechanisms in the feature fusion module to enhance the detection ability of small targets; the loss function is also improved to enhance the model’s iterative optimization ability. Test results show that the improved YOLOX model has significantly improved accuracy in projectile recognition compared to the original network, reaching 84.82%, demonstrating the feasibility of the proposed approach. The improved algorithm can be effectively used for small target scenarios in range testing and significantly improves the accuracy of recognition.https://www.frontiersin.org/articles/10.3389/fphy.2023.1213489/fulltarget detectiondeep learningYOLOXattention mechanismloss function |
| spellingShingle | Jie Hu Jie Hu Hua Chen Yubo Xu Yu Fu A bullet recognition algorithm based on improved YOLOX Frontiers in Physics target detection deep learning YOLOX attention mechanism loss function |
| title | A bullet recognition algorithm based on improved YOLOX |
| title_full | A bullet recognition algorithm based on improved YOLOX |
| title_fullStr | A bullet recognition algorithm based on improved YOLOX |
| title_full_unstemmed | A bullet recognition algorithm based on improved YOLOX |
| title_short | A bullet recognition algorithm based on improved YOLOX |
| title_sort | bullet recognition algorithm based on improved yolox |
| topic | target detection deep learning YOLOX attention mechanism loss function |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2023.1213489/full |
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