| Titre : |
Lightweight detection model for animal wet-blue hide surface defects based on Yolov5s |
| Type de document : |
texte imprimé |
| Auteurs : |
Qixin Han, Auteur ; Yushan Wan, Auteur ; Luwen Cao, Auteur ; Rong Luo, Auteur ; Yafei Sun, Auteur ; Weikuan Jia, Auteur |
| Année de publication : |
2024 |
| Article en page(s) : |
p. 255-267 |
| Note générale : |
Bibliogr. |
| Langues : |
Américain (ame) |
| Catégories : |
Cuirs et peaux -- Défauts
Détection de défauts (Ingénierie)
Surfaces -- défauts
Wet-blue (tannage)Peau tannée au chrome (le chrome donne une couleur bleue)
|
| Index. décimale : |
675 Technologie du cuir et de la fourrure |
| Résumé : |
In the process of animal leather processing, the surface damage of wet-blue hides restricts the quality of leather products. To ensure the efficiency and quality of animal leather processing, a lightweight model for detecting surface defects on wet-blue hides based on optimized YOLOv5s is proposed. The new model adopts the lightweight EfficientNetV2 network to extract surface defect features and incorporates a spatial pyramid pooling–fast (SPPF) structure at the end of the network to obtain features at different scales. Efficient multi-scale attention (EMA) was embedded in the bottom-up structure of the Neck section to achieve comprehensive feature extraction and retention, ensuring that spatial semantic features are adequately distributed in each feature. A dataset of wet-blue hide defects was constructed and used to verify the performance of the new model. the experimental results show that, the new model is superior to the commonly used classical detection models. The precision rates for detecting three types of leather surface defects, namely imprint, puncture, and breakage, are 86.5%, 95.3%, and 87.9%, respectively. These results can provide technical support for research of surface damage detection in other leather processing applications. |
| Note de contenu : |
- The wet-blue hide defect dataset : Image acquisition - Data augmentation - Dataset creation
- Lightweight detection model for surface defects on wet-blue hides : Defect feature extraction architecture - EMA attention module - The CIoU loss function
- Experiments : Experimental operating platform - Details of the experimental implementation - Evaluation criteria - Ablation studies - Comparison experiments
- Table 1 : Statistical summary of defect qantities by different size
- Table 2 : The architecture of the EfficientNetV2 network
- Table 3 : Results of comparative evaluations for defect detection in three leather categories
- Table 4 : The impact of the efficientNetV2 and EMA modules on the experimental results
- Table 5 : The detection results of both classical and state-of-the-art detection models for leather defects
- Table 6 : The parameter count, computational complexity, and detection performance outcomes of the detection model |
| DOI : |
https://doi.org/10.34314/qwaj7v95 |
| En ligne : |
https://drive.google.com/file/d/1gXRnNg4XxGBzOpLz-N3SxxMK6JV0b20L/view?usp=drive [...] |
| Format de la ressource électronique : |
Pdf |
| Permalink : |
https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=40987 |
in JOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION (JALCA) > Vol. CXIX, N° 6 (06/2024) . - p. 255-267
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