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.

Related papers

• HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection [12.865603495310328]
  HD-YOLO consists of Defect Aware Module (DAM) and Mixed Graph Network (MGNet) in the backbone. HGANet combines hypergraph and attention mechanism to aggregate multi-scale features. Cross-Scale Fusion (CSF) is proposed to adaptively fuse and handle features instead of simple concatenation and convolution.
  arXiv  Detail & Related papers  (2024-12-05T08:38:01Z)
• Fab-ME: A Vision State-Space and Attention-Enhanced Framework for Fabric Defect Detection [4.272401529389713]
  We propose Fab-ME, an advanced framework based on YOLOv8s for accurate detection of 20 fabric defect types. Our contributions include the introduction of the cross-stage partial bottleneck with two convolutions (C2F) vision state-space (C2F-VMamba) module. Experimental results on the Tianchi fabric defect detection dataset demonstrate that Fab-ME achieves a 3.5% improvement in mAP@0.5 compared to the original YOLOv8s.
  arXiv  Detail & Related papers  (2024-12-04T10:40:17Z)
• Efficient Diffusion as Low Light Enhancer [63.789138528062225]
  Reflectance-Aware Trajectory Refinement (RATR) is a simple yet effective module to refine the teacher trajectory using the reflectance component of images. textbfReflectance-aware textbfDiffusion with textbfDistilled textbfTrajectory (textbfReDDiT) is an efficient and flexible distillation framework tailored for Low-Light Image Enhancement (LLIE)
  arXiv  Detail & Related papers  (2024-10-16T08:07:18Z)
• YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection [0.0]
  Existing detection methods for insulator defect identification from unmanned aerial vehicles struggle with complex background scenes and small objects. This paper proposes a new attention-based foundation architecture, YOLO-ELA, to address this issue. Experimental results on high-resolution UAV images show that our method achieved a state-of-the-art performance of 96.9% mAP0.5 and a real-time detection speed of 74.63 frames per second.
  arXiv  Detail & Related papers  (2024-10-15T16:00:01Z)
• LSM-YOLO: A Compact and Effective ROI Detector for Medical Detection [8.812471041082105]
  We propose a novel model named Lightweight Shunt Matching-YOLO (LSM-YOLO), with Lightweight Adaptive Extraction (LAE) and Multipath Shunt Feature Matching (MSFM) Experimental results demonstrate that LSM-YOLO achieves 48.6% AP on a private dataset of pancreatic tumors, 65.1% AP on the BCCD blood cell detection public dataset, and 73.0% AP on the Br35h brain tumor detection public dataset.
  arXiv  Detail & Related papers  (2024-08-26T08:16:58Z)
• SaRPFF: A Self-Attention with Register-based Pyramid Feature Fusion module for enhanced RLD detection [0.3262230127283452]
  SaRPFF (Self-Attention with Register-based Pyramid Feature Fusion) is a novel module designed to enhance multi-scale object detection. It integrates 2D-Multi-Head Self-Attention (MHSA) with Register tokens, improving feature interpretability. Our approach demonstrates a +2.61% improvement in Average Precision (AP) on the MRLD dataset compared to the baseline FPN method in YOLOv7.
  arXiv  Detail & Related papers  (2024-02-26T04:18:42Z)
• Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects [69.39099029406248]
  We propose a joint attention-guided feature fusion network (JAFFNet) for saliency detection of surface defects based on the encoder-decoder network. JAFFNet mainly incorporates a joint attention-guided feature fusion (JAFF) module into decoding stages to adaptively fuse low-level and high-level features. Experiments conducted on SD-saliency-900, Magnetic tile, and DAGM 2007 indicate that our method achieves promising performance in comparison with other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-05T08:10:16Z)
• Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices [72.61177465035031]
  We propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy.
  arXiv  Detail & Related papers  (2023-10-21T12:07:04Z)
• MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection [54.52102265418295]
  We propose a novel and effective Multi-Level Fusion network, named as MLF-DET, for high-performance cross-modal 3D object DETection. For the feature-level fusion, we present the Multi-scale Voxel Image fusion (MVI) module, which densely aligns multi-scale voxel features with image features. For the decision-level fusion, we propose the lightweight Feature-cued Confidence Rectification (FCR) module, which exploits image semantics to rectify the confidence of detection candidates.
  arXiv  Detail & Related papers  (2023-07-18T11:26:02Z)
• Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network [101.53907377000445]
  Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. Existing methods result in the loss of middle-layer features due to activation functions. We propose a Feature Interaction Weighted Hybrid Network (FIWHN) to minimize the impact of intermediate feature loss on reconstruction quality.
  arXiv  Detail & Related papers  (2022-12-29T05:57:29Z)
• A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery [94.78943497436492]
  We present YOLO-S, a simple, fast and efficient network for small target detection. YOLO-S exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation. YOLO-S has an 87% decrease of parameter size and almost one half FLOPs of YOLOv3, making practical the deployment for low-power industrial applications.
  arXiv  Detail & Related papers  (2022-04-05T16:29:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.