PBD-YOLO: Dual-Strategy Integration of Multi-Scale Feature Fusion and Weak Texture Enhancement for Lightweight Particleboard Surface Defect Detection
Abstract
:1. Introduction and Literature Review
- (1)
- Existing improvement schemes based on YOLO primarily emphasize making the model lightweight or optimizing a single scale, which is unsatisfactory for detecting multi-scale defects that co-occur. When multi-scale defects coexist within a single scene, such as large oil pollution and small shavings, conventional feature fusion methods tend to dilute the features of smaller objects within the deep network [17,18]. This dilution can lead to missed detections.
- (2)
- The texture features of weak scratch defects are rarely detected. More attention tends to be directed toward defects with strong significance and notable grayscale, such as glue spots, shavings, and oil stains. This focus can be attributed to the shortcomings of existing methods, which lack an effective mechanism for enhancing edge features. The conventional convolutional operations within deep networks have a limited capacity to preserve the edge information [19,20], leading to reduced confidence in detecting defects with weaker features.
- (1)
- Based on the YOLOv10 algorithm architecture, an improved algorithm, PBD-YOLO, is proposed for particleboard surface defect detection. PBD-YOLO improves the mAP and the recall while guaranteeing the original network’s real-time performance.
- (2)
- The PBD-YOLO algorithm introduces the Space to Depth and Difference Enhance Convolution (SPDDEConv) module, which utilizes spatial partitioning, multi-branch difference convolution, and a channel fusion strategy [21,22]. This approach aims to retain more information during the down-sampling process and sharply enhance the edge texture characteristics of defects on the surface of particleboard. As a result, it improves the model’s ability to detect defects, particularly those with weak features.
- (3)
- The PBD-YOLO algorithm also introduces the Switchable Atrous Convolution (SAC) within the C2f feature extraction module. SAC looks twice at the input features with different atrous rates, and the outputs are combined via switches. Additionally, the ShareSepHead module is designed to merge feature maps of varying sizes from the neck by sharing weights. This design enhances the model’s adaptability to multi-scale defects on the particleboard surface and improves its robustness when simultaneously detecting multiple targets of different scales.
2. Materials and Methods
2.1. Materials
2.2. Image Acquisition and Procession
2.3. Data Augmentation Method
2.4. PBD-YOLO Algorithm Architecture
2.4.1. ShareSepHead Detection Head
2.4.2. C2f_SAC Module
2.4.3. SPDDEConv Module
2.5. Algorithm Evaluation Metrics
3. Experiment and Results
3.1. Experimental Details
3.2. Results of Data Augmentation
3.3. Results of Comparative Experiment
3.4. Results of Ablation Experiment
4. Discussion
4.1. Discussion of Comparative Experimental Results
4.2. Discussion of Ablation Experimental Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
YOLO | You Only Look Once |
PBD-YOLO | Particleboard Defect-You Only Look Once |
SPDDEConv | Space to Depth and Difference Enhance Convolution |
ShareSepHead | Share Separated Head |
SAC | Switchable Atrous Convolution |
C2f_SAC | C2f module with Switchable Atrous Convolution |
CBAM | Convolutional block attention module |
ANN | Artificial neural networks |
DFL | Distribution focal loss |
HDC | Horizontal difference convolution |
VDC | Vertical difference convolution |
mAP | Mean average precision |
IoU | Intersection over union |
BBox | Bounding box |
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Algorithm | mAP50 | mAP50–95 | Recall | Parameters | Time |
---|---|---|---|---|---|
Faster R-CNN | 0.702 | 0.445 | 0.541 | 84.7 M | 25.10 ms |
YOLOv5s | 0.736 | 0.492 | 0.703 | 18.6 M | 3.08 ms |
RTMDet-s | 0.836 | 0.566 | 0.676 | 39.03 M | 54.20 ms |
RT-DETR-ResNet50 | 0.793 | 0.583 | 0.776 | 86.1 M | 8.10 ms |
YOLOv10s | 0.801 | 0.575 | 0.684 | 15.9 M | 3.15 ms |
PBD-YOLO (ours) | 0.856 | 0.609 | 0.814 | 14.1 M | 3.16 ms |
Algorithm | Slim | ShareSepHead | SPDDEConv | C2f_SAC | mAP50 | mAP50–95 | Recall | Parameters | Time |
---|---|---|---|---|---|---|---|---|---|
A | - | - | - | - | 0.804 | 0.575 | 0.684 | 15.9 M | 3.15 ms |
B | √ | - | - | - | 0.756 | 0.529 | 0.690 | 9.8 M | 2.04 ms |
C | √ | √ | - | - | 0.828 | 0.562 | 0.779 | 9.8 M | 1.73 ms |
D | √ | - | √ | - | 0.817 | 0.571 | 0.743 | 11.5 M | 2.18 ms |
E | √ | - | - | √ | 0.794 | 0.559 | 0.731 | 11.1 M | 2.84 ms |
F | √ | √ | √ | - | 0.823 | 0.551 | 0.766 | 10.8 M | 2.21 ms |
G | √ | - | √ | √ | 0.847 | 0.603 | 0.795 | 15.6 M | 3.16 ms |
H | √ | √ | - | √ | 0.845 | 0.599 | 0.793 | 12.5 M | 2.58 ms |
I | √ | √ | √ | √ | 0.856 | 0.609 | 0.814 | 14.1 M | 3.16 ms |
Defect Class | mAP50 | ||||||||
---|---|---|---|---|---|---|---|---|---|
A | B | C | D | E | F | G | H | I | |
Spot-like defects | 0.756 | 0.762 | 0.843 | 0.838 | 0.789 | 0.843 | 0.837 | 0.872 | 0.832 |
Shavings | 0.800 | 0.713 | 0.862 | 0.747 | 0.819 | 0.804 | 0.905 | 0.828 | 0.837 |
Oil pollution | 0.452 | 0.441 | 0.444 | 0.415 | 0.448 | 0.460 | 0.544 | 0.509 | 0.573 |
Edge breakage | 0.954 | 0.891 | 0.963 | 0.964 | 0.936 | 0.959 | 0.964 | 0.961 | 0.961 |
Chalk marks | 0.587 | 0.823 | 0.903 | 0.894 | 0.897 | 0.887 | 0.901 | 0.915 | 0.919 |
Scratches | 0.868 | 0.756 | 0.823 | 0.914 | 0.741 | 0.863 | 0.831 | 0.881 | 0.903 |
Cracks | 0.939 | 0.903 | 0.955 | 0.949 | 0.926 | 0.948 | 0.947 | 0.951 | 0.970 |
Defect Class | Recall | ||||||||
---|---|---|---|---|---|---|---|---|---|
A | B | C | D | E | F | G | H | I | |
Spot-like defects | 0.595 | 0.770 | 0.785 | 0.770 | 0.754 | 0.803 | 0.689 | 0.852 | 0.820 |
Shavings | 0.588 | 0.571 | 0.801 | 0.667 | 0.762 | 0.714 | 0.902 | 0.762 | 0.759 |
Oil pollution | 0.295 | 0.375 | 0.425 | 0.276 | 0.381 | 0.339 | 0.46 | 0.446 | 0.503 |
Edge breakage | 0.900 | 0.833 | 0.933 | 0.933 | 0.906 | 0.967 | 0.933 | 0.967 | 0.933 |
Chalk marks | 0.750 | 0.750 | 0.732 | 0.786 | 0.821 | 0.826 | 0.807 | 0.786 | 0.857 |
Scratches | 0.773 | 0.694 | 0.829 | 0.829 | 0.657 | 0.771 | 0.830 | 0.794 | 0.883 |
Cracks | 0.889 | 0.833 | 0.944 | 0.944 | 0.833 | 0.944 | 0.944 | 0.944 | 0.944 |
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Guo, H.; Chai, Z.; Dai, H.; Yan, L.; Cheng, P.; Yang, J. PBD-YOLO: Dual-Strategy Integration of Multi-Scale Feature Fusion and Weak Texture Enhancement for Lightweight Particleboard Surface Defect Detection. Appl. Sci. 2025, 15, 4343. https://doi.org/10.3390/app15084343
Guo H, Chai Z, Dai H, Yan L, Cheng P, Yang J. PBD-YOLO: Dual-Strategy Integration of Multi-Scale Feature Fusion and Weak Texture Enhancement for Lightweight Particleboard Surface Defect Detection. Applied Sciences. 2025; 15(8):4343. https://doi.org/10.3390/app15084343
Chicago/Turabian StyleGuo, Haomeng, Zheming Chai, Huize Dai, Lei Yan, Pengle Cheng, and Jianhua Yang. 2025. "PBD-YOLO: Dual-Strategy Integration of Multi-Scale Feature Fusion and Weak Texture Enhancement for Lightweight Particleboard Surface Defect Detection" Applied Sciences 15, no. 8: 4343. https://doi.org/10.3390/app15084343
APA StyleGuo, H., Chai, Z., Dai, H., Yan, L., Cheng, P., & Yang, J. (2025). PBD-YOLO: Dual-Strategy Integration of Multi-Scale Feature Fusion and Weak Texture Enhancement for Lightweight Particleboard Surface Defect Detection. Applied Sciences, 15(8), 4343. https://doi.org/10.3390/app15084343