A Fabric Defect Detection System Based Improved YOLOv5 Detector
Ying Wang,Zhengyang Hao,Fang Zuo,Shanshan Pan +3 more
- Vol. 2010, Iss: 1, pp 012191
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TLDR
Wang et al. as discussed by the authors proposed an improved YOLOv5 object detection algorithm for fabric defects, which can quickly and accurately improve the accuracy of fabric defect detection and defect localization.Abstract:
Fabric defect detection is a key part of product quality assessment in the textile industry. It is important to achieve fast, accurate and efficient detection of fabric defects to improve productivity in the textile industry. For the problems of irregular shapes and many small objects, an improved YOLOv5 object detection algorithm for fabric defects is propose. In order to improve the detection accuracy of small objects, the ASFF(Adaptively Spatial Feature Fusion) feature fusion method is adopted to improve the PANet's bad effect on multi-scale feature fusion. The transformer mechanisms can enhance fused features, allowing the network to focus on useful information. Experimental results show that the mean average precision of the improved YOLOv5 object detection algorithm in fabric defect map detection is 71.70%. The improved algorithm can quickly and accurately improve the accuracy of fabric defect detection and the accuracy of defect localization.read more
Citations
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Journal ArticleDOI
Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO
TL;DR: Wang et al. as discussed by the authors proposed an improved You Only Look Once (YOLO) target detection algorithm using a combined data augmentation method to expand the dataset and improve the robustness of the algorithm, obtaining the anchors suitable for fabric defect detection by using the k-means algorithm to cluster the ground truth box of the dataset.
Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO
TL;DR: An improved YOLOv4 target detection algorithm is proposed using a combined data augmentation method to expand the dataset and improve the robustness of the algorithm, obtaining the anchors suitable for fabric defect detection by using the k-means algorithm to cluster the ground truth box of the dataset, adding a new prediction layer in yolo_head in order to have a better effect on tiny target detection.
Journal ArticleDOI
Research on improving YOLOv5s algorithm for fabric defect detection
TL;DR: Deep learning technology for automatically detecting fabric defects by improving the YOLOv5s target detection algorithm is proposed, which can effectively detect fabric defects and reduces the missed detection rate of fabric defects, improve the detection efficiency and has certain industrial value.
Proceedings ArticleDOI
Fabric Defect Defection based on Lightweight Convolutional Denoising Auto-Encoder
TL;DR: Experimental results show that the unsupervised fabric defect detection method based on lightweight convolutional denoising auto-encoder can effectively detect and localize defects with less computation and has the potential to perform defect detection on edge-embedded platforms.
Journal ArticleDOI
An Energy-Saving Road-Lighting Control System Based on Improved YOLOv5s
TL;DR: In this article , the authors proposed a control system with high intelligence and efficiency, by incorporating improved YOLOv5s with terminal embedded devices and designing a new dimming method, which achieved the highest cognition recall of 67.94%, precision of 81.28%, 74.53%AP50, and frames per second (FPS) of 59 in the DAIR-V2X dataset.
References
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Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Journal ArticleDOI
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Proceedings ArticleDOI
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Book ChapterDOI
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
TL;DR: The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.
Posted Content
Fast R-CNN
TL;DR: This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection that builds on previous work to efficiently classify object proposals using deep convolutional networks.