Defect detection as a part of industrial production is essential to monitor product quality. In the last decade, convolutional neural networks have been widely used in industrial defect detection, but existing CNNS-based fabric defect detection models suffer from the problems of not fully utilizing contextual information and inadequate characterization of the underlying features. To address the issues above, we propose a novel hierarchical feature fusion network with receptive field block is proposed for fabric defect detection. The low-level features are efficiently characterized by designing the context-aware feature extraction module (CAFEM) with a short connection. Then the novel receptive field block with five branches (RFB-5) can integrate different scale high-level feature maps, and the holistic attention module (HAM) is adopted to focus on the significant information. Moreover, the low-level features are fused with the high-level features to better represent the fabric texture information. Finally, a joint loss with a boundary IoU loss and a cross entropy loss is adopted to guide the network to learn more detailed information. Experimental results conducted on our built fabric image dataset demonstrate that the proposed method makes it possible to locate the defective areas in the fabric, which is superior to the seven existing methods.
Fabric defect detection is an essential step of quality control in the textile manufacturing industry. The fabric image texture and defects are complex and diverse, which result in poor detection results and low efficiency of the traditional fabric defect detection algorithm. Visual saliency model can quickly outstand the salient object from the complex background, and has been proven applicable in fabric defect detection. However, the existing saliency detection models still confront great challenges in boundary refinement and line-shaped defect detection. Therefore, a novel saliency-based fabric defect detection network with feature pyramid learning and refinement module is proposed to powerfully characterize features and refine boundary, in which a scale-correlated feature pyramid module (SCFPM) with cross-level connections is proposed to effectively characterize the multi-scale features from the backbone network. Moreover, an auxiliary refinement module (ARM) is designed to further refine and strengthen the input features. Finally, we incorporated the hand-crafted saliency priors to guide the network to generate the accurate saliency maps. Extensive experiments on the built fabric image datasets demonstrate that our proposed model outperforms most state-of-the-art methods under different evaluation metrics.
In recent studies, remote sensing object detection methods based on deep learning have emerged as a primary concern in environmental monitoring, military investigation, and hazard response. However, many difficulties, such as complex backgrounds, dense target quantities, large-scale variations, and non-uniform distribution, lead to many parameters and complex network structures, thus limiting the accuracy of the detector and slowing the inference speed. To address these issues, we propose a lightweight and efficient object detector for remote sensing images. First, an asymmetric convolution with the visual attention mechanism is reconstructed to decrease the complexity and strengthen the feature representation ability. Then, an adaptive feature selection structure is designed to extract discriminative feature information, which can adaptively model the shapes of objects by introducing deformable convolution to obtain a stronger geometric feature representation. To reduce information loss across different channels and spatial locations, a hybrid receptive field module is also proposed to increase the receptive field model by mixing dilated convolutional layers with different dilation rates. Finally, experimental results on the DIOR dataset show that our approach significantly improves detection accuracy and running speed.
Infrared small target detection (IRSTD) plays an essential role in many fields such as air guidance, tracking, and surveillance. However, due to the tiny sizes of infrared small targets, which are easily confused with background noises and lack clear contours and texture information, how to learn more discriminative small target features while suppressing background noises is still a challenging task. In this paper, a context-aware cross-level attention fusion network for IRSTD is proposed. Specifically, a self-attention-induced global context-aware module obtains multilevel attention feature maps with robust positional relationship modeling. The high-level feature maps with abundant semantic information are then passed through a multiscale feature refinement module to restore the target details and highlight salient features. Feature maps at all levels are fed into a channel and spatial filtering module to compress redundant information and remove background noises, which are then used for cross-level feature fusion. Furthermore, to overcome the lack of publicly available datasets, a large-scale multiscene infrared small target dataset with high-quality annotations is constructed. Finally, extensive experiments on both public and our self-developed datasets demonstrate the effectiveness of the proposed method and the superiority compared with other state-of-the-art approaches.
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