Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects

• URL: http://arxiv.org/abs/2309.12641v1
• Date: Fri, 22 Sep 2023 06:19:11 GMT
• Title: Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects
• Authors: Feng Yan, Xiaoheng Jiang, Yang Lu, Lisha Cui, Shupan Li, Jiale Cao, Mingliang Xu, and Dacheng Tao
• Abstract summary: We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
• Score: 70.48554424894728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surface defect inspection is a very challenging task in which surface defects usually show weak appearances or exist under complex backgrounds. Most high-accuracy defect detection methods require expensive computation and storage overhead, making them less practical in some resource-constrained defect detection applications. Although some lightweight methods have achieved real-time inference speed with fewer parameters, they show poor detection accuracy in complex defect scenarios. To this end, we develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The proposed DSA performs element-wise similarity in channel dimension while maintaining linear complexity. In addition, we introduce a novel Channel Reference Attention (CRA) module before each decoder block to strengthen the representation of multi-level features in the bottom-up path. The proposed CRA exploits the channel correlation between features at different layers to adaptively enhance feature representation. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods. Specifically, GCANet achieves competitive accuracy (91.79% $F_{\beta}^{w}$, 93.55% $S_\alpha$, and 97.35% $E_\phi$) on SD-saliency-900 while running 272fps on a single gpu.

Related papers

• Learning to Make Keypoints Sub-Pixel Accurate [80.55676599677824]
  This work addresses the challenge of sub-pixel accuracy in detecting 2D local features. We propose a novel network that enhances any detector with sub-pixel precision by learning an offset vector for detected features.
  arXiv  Detail & Related papers  (2024-07-16T12:39:56Z)
• MINet: Multi-scale Interactive Network for Real-time Salient Object Detection of Strip Steel Surface Defects [10.550627100986894]
  We devise a multi-scale interactive (MI) module, which employs depthwise convolution (DWConv) and pointwise convolution (PWConv) to independently extract and interactively fuse features of different scales. We propose a lightweight Multi-scale Interactive Network (MINet) to conduct real-time salient object detection of strip steel surface defects.
  arXiv  Detail & Related papers  (2024-05-25T07:09:11Z)
• DANet: Enhancing Small Object Detection through an Efficient Deformable Attention Network [0.0]
  We propose a comprehensive strategy by synergizing Faster R-CNN with cutting-edge methods. By combining Faster R-CNN with Feature Pyramid Network, we enable the model to handle multi-scale features intrinsic to manufacturing environments. Deformable Net is used that contorts and conforms to the geometric variations of defects, bringing precision in detecting even the minuscule and complex features.
  arXiv  Detail & Related papers  (2023-10-09T14:54:37Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Semi-Supervised and Long-Tailed Object Detection with CascadeMatch [91.86787064083012]
  We propose a novel pseudo-labeling-based detector called CascadeMatch. Our detector features a cascade network architecture, which has multi-stage detection heads with progressive confidence thresholds. We show that CascadeMatch surpasses existing state-of-the-art semi-supervised approaches in handling long-tailed object detection.
  arXiv  Detail & Related papers  (2023-05-24T07:09:25Z)
• Defect Transformer: An Efficient Hybrid Transformer Architecture for Surface Defect Detection [2.0999222360659604]
  We propose an efficient hybrid transformer architecture, termed Defect Transformer (DefT), for surface defect detection. DefT incorporates CNN and transformer into a unified model to capture local and non-local relationships collaboratively. Experiments on three datasets demonstrate the superiority and efficiency of our method compared with other CNN- and transformer-based networks.
  arXiv  Detail & Related papers  (2022-07-17T23:37:48Z)
• EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection [13.357235715178584]
  We re-examine the effectiveness of the standard convolutional block as a lightweight backbone architecture for face detection. We show that heavily channel-pruned standard convolution layers can achieve better accuracy and inference speed. Our proposed detector EResFD obtained 80.4% mAP on WIDER FACE Hard subset which only takes 37.7 ms for VGA image inference on CPU.
  arXiv  Detail & Related papers  (2022-04-04T02:30:43Z)
• A Cascaded Zoom-In Network for Patterned Fabric Defect Detection [8.789819609485225]
  We propose a two-step Cascaded Zoom-In Network (CZI-Net) for patterned fabric defect detection. In the CZI-Net, the Aggregated HOG (A-HOG) and SIFT features are used to instead of simple convolution filters for feature extraction. Experiments based on real-world datasets are implemented and demonstrate that our proposed method is not only computationally simple but also with high detection accuracy.
  arXiv  Detail & Related papers  (2021-08-15T15:29:26Z)
• AQD: Towards Accurate Fully-Quantized Object Detection [94.06347866374927]
  We propose an Accurate Quantized object Detection solution, termed AQD, to get rid of floating-point computation. Our AQD achieves comparable or even better performance compared with the full-precision counterpart under extremely low-bit schemes.
  arXiv  Detail & Related papers  (2020-07-14T09:07:29Z)
• End-to-End Object Detection with Transformers [88.06357745922716]
  We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss.
  arXiv  Detail & Related papers  (2020-05-26T17:06:38Z)

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.