A Lightweight and Accurate Recognition Framework for Signs of X-ray Weld Images
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Abstract (arXiv)
X-ray images are commonly used to ensure the security of devices in quality inspection industry. The recognition of signs printed on X-ray weld images plays an essential role in digital traceability system of manufacturing industry. However, the scales of objects vary different greatly in weld images, and it hinders us to achieve satisfactory recognition. In this paper, we propose a signs recognition framework based on convolutional neural networks (CNNs) for weld images. The proposed framework firstly contains a shallow classification network for correcting the pose of images. Moreover, we present a novel spatial and channel enhancement (SCE) module to address the above scale problem. This module can integrate multi-scale features and adaptively assign weights for each feature source. Based on SCE module, a narrow network is designed for final weld information recognition. To enhance the practicability of our framework, we carefully design the architecture of framework with a few parameters and computations. Experimental results show that our framework achieves 99.7% accuracy with 1.1 giga floating-point of operations (GFLOPs) on classification stage, and 90.0 mean average precision (mAP) with 176.1 frames per second (FPS) on recognition stage.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 34 references that are in the map. First posted on arXiv 18 Oct 2021.
Builds on
25 of 34 references.
References · 25
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2016Feature Pyramid Networks for Object Detectioncited by 5,036
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
- 2017Squeeze-and-Excitation Networkscited by 4,789
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
- 2015SSD: Single Shot MultiBox Detectorcited by 3,130
- 2018YOLOv3: An Incremental Improvementcited by 2,659
13 more
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
- 2016YOLO9000: Better, Faster, Strongercited by 1,431
- 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,385
- 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,276
- 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
- 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,136
- 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,060
- 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
- 2019GhostNet: More Features from Cheap Operationscited by 410
- 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
- 2020Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Trainingcited by 103
- 2016A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detectioncited by 83
- 2015Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networkscited by 80
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.