YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray Images
The detection of small voids or defects in X-ray images of tooth root canals still faces challenges. To address the issue, this paper proposes an improved YOLOv10 that combines Token Attention with Residual Convolution (ResConv), termed YOLO-TARC. To overcome the limitations of existing deep learnin...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/10/3036 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849327035154956288 |
|---|---|
| author | Yin Pan Zhenpeng Zhang Xueyang Zhang Zhi Zeng Yibin Tian |
| author_facet | Yin Pan Zhenpeng Zhang Xueyang Zhang Zhi Zeng Yibin Tian |
| author_sort | Yin Pan |
| collection | DOAJ |
| description | The detection of small voids or defects in X-ray images of tooth root canals still faces challenges. To address the issue, this paper proposes an improved YOLOv10 that combines Token Attention with Residual Convolution (ResConv), termed YOLO-TARC. To overcome the limitations of existing deep learning models in effectively retaining key features of small objects and their insufficient focusing capabilities, we introduce three improvements. First, ResConv is designed to ensure the transmission of discriminative features of small objects during feature propagation, leveraging the ability of residual connections to transmit information from one layer to the next. Second, to tackle the issue of weak focusing capabilities on small targets, a Token Attention module is introduced before the third small object detection head. By tokenizing feature maps and enhancing local focusing, it enables the model to pay closer attention to small targets. Additionally, to optimize the training process, a bounding box loss function is adopted to achieve faster and more accurate bounding box predictions. YOLO-TARC simultaneously enhances the ability to retain detailed information of small targets and improves their focusing capabilities, thereby increasing detection accuracy. Experimental results on a private root canal X-ray image dataset demonstrate that YOLO-TARC outperforms other state-of-the-art object detection models, achieving a 7.5% improvement to 80.8% in mAP50 and a 6.2% increase to 80.0% in Recall. YOLO-TARC can contribute to more accurate and efficient objective postoperative evaluation of root canal treatments. |
| format | Article |
| id | doaj-art-dfab1816cfd745528d72fe13367bd554 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-dfab1816cfd745528d72fe13367bd5542025-08-20T03:47:58ZengMDPI AGSensors1424-82202025-05-012510303610.3390/s25103036YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray ImagesYin Pan0Zhenpeng Zhang1Xueyang Zhang2Zhi Zeng3Yibin Tian4School of Computer and Information Science, Chongqing Normal University, Chongqing 401331, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaDepartment of Stomatology, The First People’s Hospital of Shunde, Southern Medical University, Foshan 528000, ChinaSchool of Computer and Information Science, Chongqing Normal University, Chongqing 401331, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaThe detection of small voids or defects in X-ray images of tooth root canals still faces challenges. To address the issue, this paper proposes an improved YOLOv10 that combines Token Attention with Residual Convolution (ResConv), termed YOLO-TARC. To overcome the limitations of existing deep learning models in effectively retaining key features of small objects and their insufficient focusing capabilities, we introduce three improvements. First, ResConv is designed to ensure the transmission of discriminative features of small objects during feature propagation, leveraging the ability of residual connections to transmit information from one layer to the next. Second, to tackle the issue of weak focusing capabilities on small targets, a Token Attention module is introduced before the third small object detection head. By tokenizing feature maps and enhancing local focusing, it enables the model to pay closer attention to small targets. Additionally, to optimize the training process, a bounding box loss function is adopted to achieve faster and more accurate bounding box predictions. YOLO-TARC simultaneously enhances the ability to retain detailed information of small targets and improves their focusing capabilities, thereby increasing detection accuracy. Experimental results on a private root canal X-ray image dataset demonstrate that YOLO-TARC outperforms other state-of-the-art object detection models, achieving a 7.5% improvement to 80.8% in mAP50 and a 6.2% increase to 80.0% in Recall. YOLO-TARC can contribute to more accurate and efficient objective postoperative evaluation of root canal treatments.https://www.mdpi.com/1424-8220/25/10/3036root canal treatmentX-rayvoid detectiondeep neural networkYOLOresidual convolution |
| spellingShingle | Yin Pan Zhenpeng Zhang Xueyang Zhang Zhi Zeng Yibin Tian YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray Images Sensors root canal treatment X-ray void detection deep neural network YOLO residual convolution |
| title | YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray Images |
| title_full | YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray Images |
| title_fullStr | YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray Images |
| title_full_unstemmed | YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray Images |
| title_short | YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray Images |
| title_sort | yolo tarc yolov10 with token attention and residual convolution for small void detection in root canal x ray images |
| topic | root canal treatment X-ray void detection deep neural network YOLO residual convolution |
| url | https://www.mdpi.com/1424-8220/25/10/3036 |
| work_keys_str_mv | AT yinpan yolotarcyolov10withtokenattentionandresidualconvolutionforsmallvoiddetectioninrootcanalxrayimages AT zhenpengzhang yolotarcyolov10withtokenattentionandresidualconvolutionforsmallvoiddetectioninrootcanalxrayimages AT xueyangzhang yolotarcyolov10withtokenattentionandresidualconvolutionforsmallvoiddetectioninrootcanalxrayimages AT zhizeng yolotarcyolov10withtokenattentionandresidualconvolutionforsmallvoiddetectioninrootcanalxrayimages AT yibintian yolotarcyolov10withtokenattentionandresidualconvolutionforsmallvoiddetectioninrootcanalxrayimages |