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Fabric defect detection is an important part of the textile industry to ensure product quality. To solve the problems such as the difficulty of detecting small defects and the coexistence of multi-scale defects in fabric defect detection, a fabric defect detection method based on the feature purification fusion structure is proposed in this paper. Specifically, we improve the feature extraction network to enhance the network's ability to focus on small defective features and effectively reduce the model parameters. The existing methods use direct fusion between multi-level feature maps, which will lead to feature confusion. Therefore, we propose the feature purification fusion structure (FPF), which includes the semantic information supplementation strategy (SIS) and the detail information supplementation strategy (DIS). SIS extracts valid information from the deep feature map and supplements it to the shallow feature map, weakening feature information irrelevant to the shallow feature map detection task. DIS adaptively supplements the feature information required by the deep feature map detection task from the shallow feature map. FPF improves the ability of the network to detect small defects and effectively mitigates the aliasing effect generated during feature fusion. The experimental results show that compared to the baseline model YOLOv5s algorithm, our model achieved a 6.8% improvement in detection accuracy, with an average inference frame rate of 37.6 FPS, demonstrating better detection performance in fabric defect detection. Furthermore, extending this model to aluminum profile defect datasets also demonstrates strong performance. [ABSTRACT FROM AUTHOR]

3D point cloud registration is a basic task in computer vision. In recent years, various of learning-based methods have been proposed to solve this problem. These methods effectively overcome the original problem of over-reliance on initial conditions and enhance the ability of obtaining the corresponding relationship. However, few methods pay enough attention to local features and tend to cause some mismatches. Therefore, this paper proposes two networks to extract local features sufficiently. To obtain a more accurate correspondence relationship between the point clouds, we propose a feature weight allocation network (FWANet), in which the expression ability of feature is enhanced using the proposed significant feature extraction module. Besides that, we utilize an interference elimination module to remove the interference points and enhance the internal correlation of point clouds. We also propose a spatial structural generation network (SSGNet), which fully utilizes the spatial location information to determine the spatial correspondence and generate a reliable connection after concatenating multi-dimensional features. At last, a complete feature space can be effectively captured after combining our FWANet with SSGNet together. We conducted multiple experiments on ModelNet40 datasets and achieved excellent results. Experimental results on four types of data demonstrate the superiority of our algorithm against the state-of-the-art ones. Our code will be available at https://github.com/liu-zikang/registration as soon as the paper is accepted. [ABSTRACT FROM AUTHOR]

The geometric distortion in panoramic images significantly mediates the performance of saliency detection method based on traditional CNN. The strategy of dynamically expanding convolution kernel can achieve good results, but it also produces a lot of computational overhead in the process of reading the adjacency list, which decreases the computational efficiency. The appearance of graph convolution provides a new way to solve such problems. Although using graph convolution can effectively extract the structural features of the graph, it reduces the accuracy of the model resulting from ignoring the spatial features of the image signal. To this end, this paper proposes a construction method of the multi-scale graph structure of the panoramic image and a panoramic image saliency detection model composed of an image saliency feature extraction network and multi-scale saliency feature fusion network combining the image structure information and spatial information in the panoramic image. First, we establish a graph structure consisting of root and leaf nodes obtained by super-pixel segmentation at different scales and spherical Fibonacci sampling, respectively. Then, a feature extraction network composed of two graph convolution layers and two one-dimensional auto-encoders with the same parameterization is used to extract the salient features of the multi-scale graph structure. Finally, the U-Net network fuses the multi-scale saliency features to get the final saliency map. The results show that the proposed model performs better than the state-of-the-art models in terms of calculation speed and accuracy. [ABSTRACT FROM AUTHOR]

Surface defect detection of industrial products has become a promising area of research. Among the existing defect detection algorithms, most of the CNN-based methods can achieve the task of defect detection under ideal experimental conditions. However, the accuracy of defect detection is easily affected by the different lighting conditions of the environment and the inconsistency of the defect scale. Therefore, general deep learning methods have difficulties in solving the problem of defect detection in complex scenes. In this paper, a defect detection network based on low-light enhancement and multi-scale feature extraction (LE–MSFE–DDNet) is proposed. There are two blocks in the proposed network, including a low-light enhancement block and a SE-FP block. In the low-light enhancement block, the deep network is applied to enhance light adaptation of the deconstructed low-light feature map in our network. The influence of illumination inconsistency is weakened by the introduction of this block. In the SE-FP block, the dependencies between different channels are combined with the multi-scale feature extraction. The defects with different scales are accurately located through the combination of this block and our network. In addition, a Fine Cans Defect dataset based on the surface of fine cans is collected by this paper to verify the feasibility of the proposed network. The proposed model is compared with the state-of-the-art object detection network and the proposed method achieves 94.3% average accuracy on the Fine Cans Defect dataset. The experimental results show that the proposed method outperforms the state-of-the-art method for surface defect detection. [ABSTRACT FROM AUTHOR]

A feature selection method based on three-way decision is proposed to solve the problem of target recognition under high-dimension and few-shot data. The main research results of this paper include the following aspects: (1) aiming at the uncertainty of existing algorithm caused by uncertain data in high-dimension and few-shot data, based on the traditional filtering feature selection ReliefF algorithm, the algorithm is improved, according to the three-way decision thought, the single threshold of original algorithm is extended into two thresholds, to increase the rate of fault tolerance of the original algorithm, avoid features is too much or too little to eliminate. (2) To solve the problem of "dimension disaster" of high-dimension and few-shot data, the features are divided into three domains according to two thresholds and feature weights, and then the features in the boundary domain are further selected by combining the wrapper feature selection idea to reduce the dimension and obtain the optimal feature subset. Finally, the experimental results show that the recognition accuracy and F1 have been improved. Experiment 1 proved the fault tolerance rate and stability of the proposed algorithm, and Experiment 2 verified the advantages of the proposed algorithm in small sample conditions. [ABSTRACT FROM AUTHOR]

Low-light images usually contain high noise and low contrast. This brings bad visual feelings and hinders subsequent computer vision work. At present, many algorithms have been proposed to enhance low-light images. However, the existing methods still have some problems, such as insufficient enhancement, color distortion, or overexposure. In this paper, we propose a low-light image enhancement network based on the spatial pyramid to solve the problems existing in other methods, so as to make the enhancement result closer to the normal illumination image in brightness and color. The network is divided into two parts. Firstly, the decomposition network is designed based on Retinex theory, and the image is decomposed into the illumination image and reflection image. Then, the illumination image is processed through the three convolution kernels on the spatial pyramid module to obtain three sets of features with different scales. Next, we concatenate these three groups of features together. And the concatenated features are extracted through a convolution kernel to obtain the enhanced illumination image. Finally, the enhanced illumination image and the decomposed reflection image are multiplied pixel by pixel to obtain an enhanced image. In addition, we introduce a color loss function to solve the problem of color distortion. The experimental results show that the proposed algorithm has better visual feelings than other algorithms. We also calculate the peak signal-to-noise ratio, structural similarity index and average brightness of the enhanced results of different algorithms, and the results show that the proposed algorithm performs better. [ABSTRACT FROM AUTHOR]