A Parts Detection Network for Switch Machine Parts in Complex Rail Transit Scenarios
The rail transit switch machine ensures the safe turning and operation of trains on the track by switching switch positions, locking switch rails, and reflecting switch status in real time. However, in the detection of complex rail transit switch machine parts such as augmented reality and automatic...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/11/3287 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849722233216303104 |
|---|---|
| author | Jiu Yong Jianwu Dang Wenxuan Deng |
| author_facet | Jiu Yong Jianwu Dang Wenxuan Deng |
| author_sort | Jiu Yong |
| collection | DOAJ |
| description | The rail transit switch machine ensures the safe turning and operation of trains on the track by switching switch positions, locking switch rails, and reflecting switch status in real time. However, in the detection of complex rail transit switch machine parts such as augmented reality and automatic inspection, existing algorithms have problems such as insufficient feature extraction, large computational complexity, and high demand for hardware resources. This article proposes a complex scene rail transit switch machine parts detection network YOLO-SMPDNet (YOLO-based Switch Machine Parts Detecting Network). The YOLOv8s backbone network is improved, and the number of network parameters are reduced by introducing MobileNetV3. Then a parameter-free attention-enhanced ResAM module is designed, which forms a lightweight detection network with the improved network, improving detection efficiency. Finally, Focal IoU Loss is introduced to more accurately define the scale information of the prediction box, alleviate the problem of imbalanced positive and negative samples, and improve the relative ambiguity of CIoU Loss in YOLOv8s on the definition of aspect ratio. By validating the performance of YOLO-SMPDNet on a self-made dataset of rail transit switch machines, the results show that YOLO-SMPDNet can significantly improve detection accuracy and real-time performance and has robust comprehensive detection capabilities for rail transit switch machine parts and good practical application performance. |
| format | Article |
| id | doaj-art-2bde0c2a07574a64a22bb127b6c75979 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-2bde0c2a07574a64a22bb127b6c759792025-08-20T03:11:24ZengMDPI AGSensors1424-82202025-05-012511328710.3390/s25113287A Parts Detection Network for Switch Machine Parts in Complex Rail Transit ScenariosJiu Yong0Jianwu Dang1Wenxuan Deng2The School of Electronic and Information Engineering, Lanzhou Jiaotong Univeristy, Lanzhou 730070, ChinaThe School of Electronic and Information Engineering, Lanzhou Jiaotong Univeristy, Lanzhou 730070, ChinaThe School of Electronic and Information Engineering, Lanzhou Jiaotong Univeristy, Lanzhou 730070, ChinaThe rail transit switch machine ensures the safe turning and operation of trains on the track by switching switch positions, locking switch rails, and reflecting switch status in real time. However, in the detection of complex rail transit switch machine parts such as augmented reality and automatic inspection, existing algorithms have problems such as insufficient feature extraction, large computational complexity, and high demand for hardware resources. This article proposes a complex scene rail transit switch machine parts detection network YOLO-SMPDNet (YOLO-based Switch Machine Parts Detecting Network). The YOLOv8s backbone network is improved, and the number of network parameters are reduced by introducing MobileNetV3. Then a parameter-free attention-enhanced ResAM module is designed, which forms a lightweight detection network with the improved network, improving detection efficiency. Finally, Focal IoU Loss is introduced to more accurately define the scale information of the prediction box, alleviate the problem of imbalanced positive and negative samples, and improve the relative ambiguity of CIoU Loss in YOLOv8s on the definition of aspect ratio. By validating the performance of YOLO-SMPDNet on a self-made dataset of rail transit switch machines, the results show that YOLO-SMPDNet can significantly improve detection accuracy and real-time performance and has robust comprehensive detection capabilities for rail transit switch machine parts and good practical application performance.https://www.mdpi.com/1424-8220/25/11/3287rail transitobject detectionMobileNetV3convolutional neural networkResAM |
| spellingShingle | Jiu Yong Jianwu Dang Wenxuan Deng A Parts Detection Network for Switch Machine Parts in Complex Rail Transit Scenarios Sensors rail transit object detection MobileNetV3 convolutional neural network ResAM |
| title | A Parts Detection Network for Switch Machine Parts in Complex Rail Transit Scenarios |
| title_full | A Parts Detection Network for Switch Machine Parts in Complex Rail Transit Scenarios |
| title_fullStr | A Parts Detection Network for Switch Machine Parts in Complex Rail Transit Scenarios |
| title_full_unstemmed | A Parts Detection Network for Switch Machine Parts in Complex Rail Transit Scenarios |
| title_short | A Parts Detection Network for Switch Machine Parts in Complex Rail Transit Scenarios |
| title_sort | parts detection network for switch machine parts in complex rail transit scenarios |
| topic | rail transit object detection MobileNetV3 convolutional neural network ResAM |
| url | https://www.mdpi.com/1424-8220/25/11/3287 |
| work_keys_str_mv | AT jiuyong apartsdetectionnetworkforswitchmachinepartsincomplexrailtransitscenarios AT jianwudang apartsdetectionnetworkforswitchmachinepartsincomplexrailtransitscenarios AT wenxuandeng apartsdetectionnetworkforswitchmachinepartsincomplexrailtransitscenarios AT jiuyong partsdetectionnetworkforswitchmachinepartsincomplexrailtransitscenarios AT jianwudang partsdetectionnetworkforswitchmachinepartsincomplexrailtransitscenarios AT wenxuandeng partsdetectionnetworkforswitchmachinepartsincomplexrailtransitscenarios |