Enhanced YOLO and Scanning Portal System for Vehicle Component Detection
In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. The system consists of a scanning portal system and an improved YOLOv12-based detection algorithm which captures images of au...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4809 |
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| author | Feng Ye Mingzhe Yuan Chen Luo Shuo Li Duotao Pan Wenhong Wang Feidao Cao Diwen Chen |
| author_facet | Feng Ye Mingzhe Yuan Chen Luo Shuo Li Duotao Pan Wenhong Wang Feidao Cao Diwen Chen |
| author_sort | Feng Ye |
| collection | DOAJ |
| description | In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. The system consists of a scanning portal system and an improved YOLOv12-based detection algorithm which captures images of automotive parts passing through the scanning portal in real time. By integrating deep learning, the system enables real-time monitoring and identification, thereby preventing misdetections and missed detections of automotive parts, in this way promoting intelligent automotive part recognition and detection. Our system introduces the A2C2f-SA module, which achieves an efficient feature attention mechanism while maintaining a lightweight design. Additionally, Dynamic Space-to-Depth (Dynamic S2D) is employed to improve convolution and replace the stride convolution and pooling layers in the baseline network, helping to mitigate the loss of fine-grained information and enhancing the network’s feature extraction capability. To improve real-time performance, a GFL-MBConv lightweight detection head is proposed. Furthermore, adaptive frequency-aware feature fusion (Adpfreqfusion) is hybridized at the end of the neck network to effectively enhance high-frequency information lost during downsampling, thereby improving the model’s detection accuracy for target objects in complex backgrounds. On-site tests demonstrate that the system achieves a comprehensive accuracy of 97.3% and an average vehicle detection time of 7.59 s, exhibiting not only high precision but also high detection efficiency. These results can make the proposed system highly valuable for applications in the automotive industry. |
| format | Article |
| id | doaj-art-b712f6974ee7491a87a0850f3ac8c84e |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b712f6974ee7491a87a0850f3ac8c84e2025-08-20T03:02:56ZengMDPI AGSensors1424-82202025-08-012515480910.3390/s25154809Enhanced YOLO and Scanning Portal System for Vehicle Component DetectionFeng Ye0Mingzhe Yuan1Chen Luo2Shuo Li3Duotao Pan4Wenhong Wang5Feidao Cao6Diwen Chen7College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, ChinaGuangzhou Institute of Industrial Intelligence, Guangzhou 511458, ChinaCollege of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, ChinaGuangzhou Institute of Industrial Intelligence, Guangzhou 511458, ChinaCollege of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, ChinaGuangzhou Institute of Industrial Intelligence, Guangzhou 511458, ChinaGuangzhou Institute of Industrial Intelligence, Guangzhou 511458, ChinaGuangzhou Institute of Industrial Intelligence, Guangzhou 511458, ChinaIn this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. The system consists of a scanning portal system and an improved YOLOv12-based detection algorithm which captures images of automotive parts passing through the scanning portal in real time. By integrating deep learning, the system enables real-time monitoring and identification, thereby preventing misdetections and missed detections of automotive parts, in this way promoting intelligent automotive part recognition and detection. Our system introduces the A2C2f-SA module, which achieves an efficient feature attention mechanism while maintaining a lightweight design. Additionally, Dynamic Space-to-Depth (Dynamic S2D) is employed to improve convolution and replace the stride convolution and pooling layers in the baseline network, helping to mitigate the loss of fine-grained information and enhancing the network’s feature extraction capability. To improve real-time performance, a GFL-MBConv lightweight detection head is proposed. Furthermore, adaptive frequency-aware feature fusion (Adpfreqfusion) is hybridized at the end of the neck network to effectively enhance high-frequency information lost during downsampling, thereby improving the model’s detection accuracy for target objects in complex backgrounds. On-site tests demonstrate that the system achieves a comprehensive accuracy of 97.3% and an average vehicle detection time of 7.59 s, exhibiting not only high precision but also high detection efficiency. These results can make the proposed system highly valuable for applications in the automotive industry.https://www.mdpi.com/1424-8220/25/15/4809target detectionautomobile parts inspectionsystem designYOLOv12n |
| spellingShingle | Feng Ye Mingzhe Yuan Chen Luo Shuo Li Duotao Pan Wenhong Wang Feidao Cao Diwen Chen Enhanced YOLO and Scanning Portal System for Vehicle Component Detection Sensors target detection automobile parts inspection system design YOLOv12n |
| title | Enhanced YOLO and Scanning Portal System for Vehicle Component Detection |
| title_full | Enhanced YOLO and Scanning Portal System for Vehicle Component Detection |
| title_fullStr | Enhanced YOLO and Scanning Portal System for Vehicle Component Detection |
| title_full_unstemmed | Enhanced YOLO and Scanning Portal System for Vehicle Component Detection |
| title_short | Enhanced YOLO and Scanning Portal System for Vehicle Component Detection |
| title_sort | enhanced yolo and scanning portal system for vehicle component detection |
| topic | target detection automobile parts inspection system design YOLOv12n |
| url | https://www.mdpi.com/1424-8220/25/15/4809 |
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