Weak Scratch Detection of Optical Components Using Attention Fusion Network

Tao, Xu; Zhang, Dapeng; Singh, Avinash Kumar; Prasad, Mukesh; Lin, Chin‐Teng; Xu, De

doi:10.1109/case48305.2020.9216781

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Although this can improve the final detection accuracy, it consumes much more computing resources. Tao et al [66] used a multi-attention mechanism to enhance the detection effect of the network on small objects. However, this method increases the difficulty of training and is difficult to apply to sophisticated scenes.…”

Section: Related Workmentioning

confidence: 99%

An Efficient End-to-End Multitask Network Architecture for Defect Inspection

Zhang

Yang

et al. 2022

Sensors

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Recently, computer vision-based methods have been successfully applied in many industrial fields. Nevertheless, automated detection of steel surface defects remains a challenge due to the complexity of surface defects. To solve this problem, many models have been proposed, but these models are not good enough to detect all defects. After analyzing the previous research, we believe that the single-task network cannot fully meet the actual detection needs owing to its own characteristics. To address this problem, an end-to-end multi-task network has been proposed. It consists of one encoder and two decoders. The encoder is used for feature extraction, and the two decoders are used for object detection and semantic segmentation, respectively. In an effort to deal with the challenge of changing defect scales, we propose the Depthwise Separable Atrous Spatial Pyramid Pooling module. This module can obtain dense multi-scale features at a very low computational cost. After that, Residually Connected Depthwise Separable Atrous Convolutional Blocks are used to extract spatial information under low computation for better segmentation prediction. Furthermore, we investigate the impact of training strategies on network performance. The performance of the network can be optimized by adopting the strategy of training the segmentation task first and using the deep supervision training method. At length, the advantages of object detection and semantic segmentation are tactfully combined. Our model achieves mIOU 79.37% and mAP@0.5 78.38% on the NEU dataset. Comparative experiments demonstrate that this method has apparent advantages over other models. Meanwhile, the speed of detection amount to 85.6 FPS on a single GPU, which is acceptable in the practical detection process.

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Section: Related Workmentioning

confidence: 99%

An Efficient End-to-End Multitask Network Architecture for Defect Inspection

Zhang

Yang

et al. 2022

Sensors

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“…The proposed method is trained in a minibatch based end-to-end manner. After the model is trained, the cluster label can be computed by Equation (12) while using the output one-hot vector of the softmax layer. The overall training steps are similar to [30], as shown below:…”

Section: Model Optimizationmentioning

confidence: 99%

“…The interferometric polarimetric SAR multi-chromatic analysis (MCA-PolInSAR) signal processing method that was proposed in [11] can confirm the feasibility to resolve the volume-oriented indetermination problem. Deep learning is a branch of machine learning and it provides the state-of-the-art solutions to many problems in natural image processing field [12,13]. It also shows excellent performance in supervised PolSAR image classification [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Learning Rotation Domain Deep Mutual Information Using Convolutional LSTM for Unsupervised PolSAR Image Classification

Wang

Gui

et al. 2020

Remote Sensing

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Deep learning can archive state-of-the-art performance in polarimetric synthetic aperture radar (PolSAR) image classification with plenty of labeled data. However, obtaining large number of accurately labeled samples of PolSAR data is very hard, which limits the practical use of deep learning. Therefore, unsupervised PolSAR image classification is worthy of further investigation that is based on deep learning. Inspired by the superior performance of deep mutual information in natural image feature learning and clustering, an end-to-end Convolutional Long Short Term Memory (ConvLSTM) network is used in order to learn the deep mutual information of polarimetric coherent matrices in the rotation domain with different polarimetric orientation angles (POAs) for unsupervised PolSAR image classification. First, for each pixel, paired “POA-spatio” samples are generated from the polarimetric coherent matrices with different POAs. Second, a special designed ConvLSTM network, along with deep mutual information losses, is used in order to learn the discriminative deep mutual information feature representation of the paired data. Finally, the classification results can be output directly from the trained network model. The proposed method is trained in an end-to-end manner and does not have cumbersome pipelines. Experiments on four real PolSAR datasets show that the performance of proposed method surpasses some state-of-the-art deep learning unsupervised classification methods.

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