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The identification of textile fiber materials is a tedious task. Traditional physical or chemical methods rely on specialist knowledge and expensive instruments. Currently, the deep learning-based methods are limited by the lack of sufficient image samples. In this paper, we propose a method for the classification of textile materials based on advanced densely connected convolutional networks (Densenet) using fabric surface images. Firstly, we selected a small sample dataset containing five types of materials and performed some pre-processing, including colour weakening and data enhancement. The lightweight network Densenet was then used as the main body to ensure the low parameters of the network, making it more suitable for the recognition of small sample image datasets. The network is then fused with the attention mechanism, Orthogonal Softmax Layer (OSL) and the depth-separable convolution mechanism, and we call the final network AONet. It has only 0.64 M (million) parameters. In this paper, we tested four metrics of AONet for five types of fabric images, and the results showed that the recognition accuracy was 95.61%. The precision, recall and F1 scores were 94.87%, 94.31% and 94.40%, respectively. [ABSTRACT FROM AUTHOR]

In the ore beneficiation process, large block detection is necessary to ensure production safety. This typically involves identifying oversized ore on the conveyor belt and preventing material blockage accidents in the transfer buffer bin between the ore feeding belt and the ore receiving belt. Methods based on deep learning can learn to construct complex features from a large amount of data, but they also require a large number of hand-made datasets for training. Although the existing few shot detection methods for ore images reduce the cost of manual labeling, the corresponding detection performance is insufficient. This article mainly explores how to improve the performance of the detector under the ore image detection task in the case of few labeled images. First, a shot enhancement block is proposed to enhance the valuable foreground information for higher-quality support features. Subsequently, we present a dual-attention region proposal network that effectively leverages support features to enhance the precision of generating candidate proposals. Finally, we propose a lightweight multi-relational detector to effectively evaluate the relationship between query and support proposals, leading to a substantial enhancement in guidance performance. The proposed few-shot object detector (FS-OreDet) achieves the best detection results with state-of-the-art methods with an average precision (A P) of 55.1, a speed of 57 frames per second (F P S), and a model size of only 17 M B. Furthermore, our framework adeptly captures the feature information of ore images with substantial data. The detector's accuracy achieves a significant improvement of 14% in A P. Compared with general object detectors, the performance of the detector ranks first and meets the requirements for outdoor scene deployment. • Proposing a stronger few-shot object detector for ore images via feature enhancement and relationship exploration. • Some components are designed for boosting few-shot object detectors of ore images. • Extensive experiments on the ore dataset and theoretical analyses are also added to verify the superiority of the proposed detectors. [ABSTRACT FROM AUTHOR]

Image segmentation approaches have been utilized to determine the particle size distribution of crushed ores in the past decades. It is not possible to deploy large and high-powered computing equipment due to the complex working environment, so existing algorithms are difficult to apply in practical engineering. This article presents a novel efficient and lightweight framework for ore image segmentation to discern full and independent ores. First, a lightweight backbone is introduced for feature extraction while reducing computational complexity. Then, we propose a compact pyramid network to process the data obtained from the backbone to reduce unnecessary semantic information and computation. Finally, an optimized detection head is proposed to obtain the feature to maintain accuracy. Extensive experimental results demonstrate the effectiveness of our method, which achieves 40 frames per second on our new ore image dataset with a very small model size. Meanwhile, our method maintains a high level of accuracy—67.68% in A P 50 b o x and 46.73% in A P 50 m a s k —compared with state-of-the-art approaches. [ABSTRACT FROM AUTHOR]