A complete defect detection task aims to achieve the specific class and precise location of each defect in an image, which makes it still challenging for applying this task in practice. The defect detection is a composite task of classification and location, leading to related methods are often hard to take into account the accuracy of both. And the implementation of defect detection depends on a special detection dataset which contains expensive manual annotations. In this paper, we proposed a novel defect detection system based on deep learning and focused on a practical industrial application: steel plate defect inspection. In order to achieve strong classification-ability, this system employs a baseline convolution neural network (CNN) to generate feature maps at each stage. And then the proposed multilevel-feature fusion network (MFN) combines multiple hierarchical features into one feature, which can include more location details of defects. Based on these multilevel features, a region proposal network (RPN) is adopted to generate regions of interest (ROIs). For each ROI, a detector, consisting of a classifier and a bounding box regressor, produces the final detection results. Finally, we set up a defect detection dataset NEU-DET for training and evaluating our method. On the NEU-DET, our method achieves 74.8/82.3 mAP with baseline networks ResNet34/50 by using 300 proposals. In addition, by using only 50 proposals, our method can detect at 20 fps on a single GPU and reach 92% of the above performance, hence the potential for real-time detection.

Surface defect detection is a critical task in industrial production process. Nowadays, there are lots of detection methods based on computer vision and have been successfully applied in industry, they also achieved good results. However, achieving full automation of surface defect detection remains a challenge, due to the complexity of surface defect, in intra-class, while the defects between inter-class contain similar parts, there are large differences in appearance of the defects. To address these issues, this paper proposes a pyramid feature fusion and global context attention network for pixel-wise detection of surface defect, called PGA-Net. In the framework, the multi-scale features are extracted at first from backbone network. Then the pyramid feature fusion module is used to fuse these features into five resolutions through some efficient dense skip connections. Finally, the global context attention module is applied to the fusion feature maps of adjacent resolution, which allows effective information propagate from low-resolution fusion feature maps to high-resolution fusion ones. In addition, the boundary refinement block is added to the framework to refine the boundary of defect and improve the result of predict. The final prediction is the fusion of the five resolutions fusion feature maps. The results of evaluation on four real-world defect datasets demonstrate that the proposed method outperforms the state-of-the-art methods on mean Intersection of Union and mean Pixel Accuracy (NEU-Seg: 82.15%, DAGM 2007: 74.78%, MT_defect: 71.31%, Road_defect: 79.54%).

It is still a challenging task to detect the surface defects of strip steel due to its complex variations, including variable defect types, cluttered background, low contrast and noise interference. The existing detection methods cannot effectively segment the defect objects from complex background and have poor real-time performance. To address these issues, we propose a novel saliency detection method based on Encoder-Decoder Residual network (EDRNet). In the encoder stage, we use fully convolutional neural network to extract rich multi-level defect features, and fuse the attention mechanism to accelerate the convergence of the model. Then in the decoder stage, we adopt the channels weighted block (CWB) and the residual decoder block (RDB) alternatively to integrate the spatial features of shallower layers and semantic features of deep layers, and recover the predicted spatial saliency values step by step. Finally, we design the residual refinement structure with 1D filters (RRS̲1D) to further optimize the coarse saliency map. Compared with the existing saliency detection methods, deeply supervised EDRNet can accurately segment the complete defect objects with well-defined boundary, and effectively filter out irrelevant background noise. Extensive experimental results prove that our method is consistently superior to the state-of-the-art methods with large margins and strong robustness, and the detection efficiency is at over 27fps on a single GPU.

Metal surface defect segmentation can play an important role in dealing with the issue of quality control during the production and manufacturing stages. There are still two major challenges in industrial applications. One is the case that the number of metal surface defect samples is severely insufficient, and the other is that the most existing algorithms can only be used for specific surface defects and it is difficult to generalize to other metal surfaces. In this work, a theory of few-shot metal generic surface defect segmentation is introduced to solve these challenges. Simultaneously, the Triplet-Graph Reasoning Network (TGRNet) and a novel dataset Surface Defects- 4 ⁱ are proposed to achieve this theory. In our TGRNet, the surface defect triplet (including triplet encoder and trip loss) is proposed and is used to segment background and defect area, respectively. Through triplet, the few-shot metal surface defect segmentation problem is transformed into few-shot semantic segmentation problem of defect area and background area. For few-shot semantic segmentation, we propose a method of multi-graph reasoning to explore the similarity relationship between different images. And to improve segmentation performance in the industrial scene, an adaptive auxiliary prediction module is proposed. For Surface Defects- 4 ⁱ , it includes multiple categories of metal surface defect images to verify the generalization performance of our TGRNet and adds the nonmetal categories (leather and tile) as extensions. Through extensive comparative experiments and ablation experiments, it is proved that our architecture can achieve state-of-the-art results.

RGB salient object detection (SOD) has made great progress. However, the performance of this single-modal salient object detection will be significantly decreased when encountering some challenging scenes, such as low light or darkness. To deal with the above challenges, thermal infrared (T) image is introduced into the salient object detection. This fused modal is called RGB-T salient object detection. To achieve deep mining of the unique characteristics of single modal and the full integration of cross-modality information, a novel Cross-Guided Fusion Network (CGFNet) for RGB-T salient object detection is proposed. Specifically, a Cross-Scale Alternate Guiding Fusion (CSAGF) module is proposed to mine the high-level semantic information and provide global context support. Subsequently, we design a Guidance Fusion Module (GFM) to achieve sufficient cross-modality fusion by using single modal as the main guidance and the other modal as auxiliary. Finally, the Cross-Guided Fusion Module (CGFM) is presented and serves as the main decoding block. And each decoding block is consists of two parts with two modalities information of each being the main guidance, i.e., cross-shared Cross-Level Enhancement (CLE) and Global Auxiliary Enhancement (GAE). The main difference between the two parts is that the GFM using different modalities as the main guide. The comprehensive experimental results prove that our method achieves better performance than the state-of-the-art salient detection methods. The source code has released at: https://github.com/wangjie0825/CGFNet.git .

Visual information has been paid more attention to rail surface defect detection for its high efficiency and stability. However, it is not sufficient to detect more complete defects in complex background information. The addition of profiles can effectively improve the above situation due to its entity information. However, in high-speed detection, traditional three-dimensional (3D) profiles acquisition is difficult and separate from image acquisition, which can not satisfy the above requirements effectively. Therefore, an unsupervised stereoscopic saliency detection method based on binocular line-scanning system is proposed in this paper. This method can obtain the extraordinary precision image and profile information at the same time, and avoid decoding distortion of the structured light reconstruction method. In this method, a global low-rank non-negative reconstruction algorithm with a background constraint is proposed. Unlike the low-rank recovery (LRR) model, the algorithm has a more comprehensive low-rank and background clustering properties. Besides, outlier detection based on the geometric properties of the rail surface is also proposed in this method. Finally, image saliency results and depth outlier detection results are associated with interactive fusion. Otherwise, data set (RSDDS-113) containing rail surface defects is established for experimental verification. The experimental results demonstrate that our method can obtain the results that MAE is 0.09 and AUC is 0.94, which is better than other 15 algorithms.