LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image

• URL: http://arxiv.org/abs/2110.15045v1
• Date: Thu, 28 Oct 2021 12:19:32 GMT
• Title: LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image
• Authors: Moyun Liu, Youping Chen, Lei He, Yang Zhang, Jingming Xie
• Abstract summary: We propose a weld defect detection method based on convolution neural network (CNN), namely Lighter and Faster YOLO (LF-YOLO) To improve the performance of detection network, we propose an efficient feature extraction (EFE) module. Experimental results show that our weld defect network achieves satisfactory balance between performance and consumption, and reaches 92.9 mAP50 with 61.5 FPS.
• Score: 7.970559381165446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: X-ray image plays an important role in manufacturing for quality assurance, because it can reflect the internal condition of weld region. However, the shape and scale of different defect types vary greatly, which makes it challenging for model to detect weld defects. In this paper, we propose a weld defect detection method based on convolution neural network (CNN), namely Lighter and Faster YOLO (LF-YOLO). In particularly, an enhanced multiscale feature (EMF) module is designed to implement both parameter-based and parameter-free multi-scale information extracting operation. EMF enables the extracted feature map capable to represent more plentiful information, which is achieved by superior hierarchical fusion structure. To improve the performance of detection network, we propose an efficient feature extraction (EFE) module. EFE processes input data with extremely low consumption, and improve the practicability of whole network in actual industry. Experimental results show that our weld defect network achieves satisfactory balance between performance and consumption, and reaches 92.9 mAP50 with 61.5 FPS. To further prove the ability of our method, we test it on public dataset MS COCO, and the results show that our LF-YOLO has a outstanding versatility detection performance. The code is available at https://github.com/lmomoy/LF-YOLO.

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