BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection
In the field of bookbinding, accurately and efficiently detecting signature sequences during the binding process is crucial for enhancing quality, improving production efficiency, and advancing industrial automation. Despite significant advancements in object detection technology, verifying the corr...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/23/10829 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850260894377836544 |
|---|---|
| author | Long Guo Lubin Wang Qiang Yu Xiaolan Xie |
| author_facet | Long Guo Lubin Wang Qiang Yu Xiaolan Xie |
| author_sort | Long Guo |
| collection | DOAJ |
| description | In the field of bookbinding, accurately and efficiently detecting signature sequences during the binding process is crucial for enhancing quality, improving production efficiency, and advancing industrial automation. Despite significant advancements in object detection technology, verifying the correctness of signature sequences remains challenging due to the small size, dense distribution, and abundance of low-quality signature marks. To tackle these challenges, we introduce the Book Signature Marks Detection (BSMD-YOLOv8) model, specifically designed for scenarios involving small, closely spaced objects such as signature marks. Our proposed backbone, the Lightweight Multi-scale Residual Network (LMRNet), achieves a lightweight network while enhancing the accuracy of small object detection. To address the issue of insufficient fusion of local and global feature information in PANet, we design the Low-stage gather-and-distribute (Low-GD) module and the High-stage gather-and-distribute (High-GD) module to enhance the model’s multi-scale feature fusion capabilities, thereby refining the integration of local and global features of signature marks. Furthermore, we introduce Wise-IoU (WIoU) as a replacement for CIoU, prioritizing anchor boxes with moderate quality and mitigating harmful gradients from low-quality examples. Experimental results demonstrate that, compared to YOLOv8n, BSMD-YOLOv8 reduces the number of parameters by 65%, increases the frame rate by 7 FPS, and enhances accuracy, recall, and mAP50 by 2.2%, 8.6%, and 3.9% respectively, achieving rapid and accurate detection of signature marks. |
| format | Article |
| id | doaj-art-b2a9d5159e994b329954e6a9e4e28794 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b2a9d5159e994b329954e6a9e4e287942025-08-20T01:55:32ZengMDPI AGApplied Sciences2076-34172024-11-0114231082910.3390/app142310829BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks DetectionLong Guo0Lubin Wang1Qiang Yu2Xiaolan Xie3College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, ChinaCollege of Information Engineering, Guilin Institute of Information Technology, Guilin 541004, ChinaNational Space Science Center, Chinese Academy of Sciences, Beijing 100190, ChinaCollege of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, ChinaIn the field of bookbinding, accurately and efficiently detecting signature sequences during the binding process is crucial for enhancing quality, improving production efficiency, and advancing industrial automation. Despite significant advancements in object detection technology, verifying the correctness of signature sequences remains challenging due to the small size, dense distribution, and abundance of low-quality signature marks. To tackle these challenges, we introduce the Book Signature Marks Detection (BSMD-YOLOv8) model, specifically designed for scenarios involving small, closely spaced objects such as signature marks. Our proposed backbone, the Lightweight Multi-scale Residual Network (LMRNet), achieves a lightweight network while enhancing the accuracy of small object detection. To address the issue of insufficient fusion of local and global feature information in PANet, we design the Low-stage gather-and-distribute (Low-GD) module and the High-stage gather-and-distribute (High-GD) module to enhance the model’s multi-scale feature fusion capabilities, thereby refining the integration of local and global features of signature marks. Furthermore, we introduce Wise-IoU (WIoU) as a replacement for CIoU, prioritizing anchor boxes with moderate quality and mitigating harmful gradients from low-quality examples. Experimental results demonstrate that, compared to YOLOv8n, BSMD-YOLOv8 reduces the number of parameters by 65%, increases the frame rate by 7 FPS, and enhances accuracy, recall, and mAP50 by 2.2%, 8.6%, and 3.9% respectively, achieving rapid and accurate detection of signature marks.https://www.mdpi.com/2076-3417/14/23/10829small object detectionYOLOv8book signature marks detectionWIoU |
| spellingShingle | Long Guo Lubin Wang Qiang Yu Xiaolan Xie BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection Applied Sciences small object detection YOLOv8 book signature marks detection WIoU |
| title | BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection |
| title_full | BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection |
| title_fullStr | BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection |
| title_full_unstemmed | BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection |
| title_short | BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection |
| title_sort | bsmd yolov8 enhancing yolov8 for book signature marks detection |
| topic | small object detection YOLOv8 book signature marks detection WIoU |
| url | https://www.mdpi.com/2076-3417/14/23/10829 |
| work_keys_str_mv | AT longguo bsmdyolov8enhancingyolov8forbooksignaturemarksdetection AT lubinwang bsmdyolov8enhancingyolov8forbooksignaturemarksdetection AT qiangyu bsmdyolov8enhancingyolov8forbooksignaturemarksdetection AT xiaolanxie bsmdyolov8enhancingyolov8forbooksignaturemarksdetection |