The efficient separation of coal and gangue in the mining process is of great significance for improving coal mining efficiency and reducing environmental pollution. Automatic detection of coal and gangue is the key and foundation for the separation of coal and gangue. In this paper, we proposed a hierar-chical framework for coal and gangue detection based on deep learning models. In this framework, the Gauss-ian pyramid principle is first used to construct multi-level training data, leading to the sets of coal and gangue image features with multiple scales. Then, the coal and gangue regional proposal networks (CG-RPN) are designed to determine the candidate regions of the target objects in the image. Next, convolution neural net-works (CNNs) are constructed to recognize coal and gangue objects on the basis of extracted candidate re-gions. We performed our method on three different datasets. Experimental results showed that the proposed method improves the detection accuracy of coal and gangue objects by 0.8% compared with the previous methods, reaching up to 98.33%. In addition, our proposed method enables the detection of multiple coal and gangue objects in an individual image and solves the problem of queuing requirements in traditional methods.

Accurate and efficient extraction of road marking plays an important role in road transportation engineering, automotive vision, and automatic driving. In this article, we proposed a dense feature pyramid network (DFPN)-based deep learning model, by considering the particularity and complexity of road marking. The DFPN concatenated its shallow feature channels with deep feature channels so that the shallow feature maps with high resolution and abundant image details can utilize the deep features. Thus, the DFPN can learn hierarchical deep detailed features. The designed deep learning model was trained end to end for road marking instance extraction with mobile laser scanning (MLS) point clouds. Then, we introduced the focal loss function into the optimization of deep learning model in road marking segmentation part, to pay more attention to the hard-classified samples with a large extent of background. In the experiments, our method can achieve better results than state-of-the-art methods on instance segmentation of road markings, which illustrated the advantage of the proposed method.

The accurate extraction of traffic signs is of great significance to the digitization of traffic information and the fine management of traffic. This article introduces an innovative approach to address the challenges associated with recognizing and detecting traffic signs, considering their vulnerability to complex backgrounds, variations in illumination, and motion blur. The proposed method utilizes a semi-supervised learning (SSL) strategy, combining convolutional neural networks (CNNs) with a transformer encoder–decoder architecture, to extract traffic sign features from vehicle panoramic images. To enhance feature extraction, a hierarchical sampling method (HSM) is introduced, which facilitates the extraction of multiscale self-attention features in the transformer encoder–decoder structure. Additionally, a network module called local and global information aggregator (LGIA) is designed based on HSM, enabling the incorporation of both local and global context information. Furthermore, a SSL strategy is adopted to simultaneously train our model using both labeled and unlabeled data samples. This strategy aims to improve the extraction of traffic signs by capitalizing on the broader data set available through unlabeled data. Experimental results demonstrate the effectiveness and robustness of the proposed method in improving the detection and recognition of traffic signs. The approach showcases significant improvements in overcoming the challenges posed by complex backgrounds, variations in illumination, and motion blur. Our approach achieved a 0.9% improvement in the F1-score evaluation over the current classical object detection algorithm on the public data set Tsinghua-Tencent 100K and a 1.1% improvement on the SSW data set.

The maintenance of subway tunnels is vital to ensure the safety of their daily operation. Issues experienced by shield subway tunnels, especially the water leakages, require rapid and accurate detection and diagnosis. Due to the large number of disturbances in the tunnels, conventional algorithms face limitations when extracting discriminative features. To solve this problem, we propose a novel and efficient deep learning model for extracting multiscale and discriminative features of water leakages based on mobile laser scanning (MLS) point cloud intensity images. A new residual network module (Res2Net) is integrated with a cascade structure to form a unified model to extract the multiscale features of water leakages. The model can fully consider geometric characteristics of water leakages and grade the residual connections in a single residual block. This expands the size of receptive field in each network layer and can better facilitate the extraction of geometric characteristics of water leakages. Finally, we verify the advantages of the proposed method via experiments on five water leakage datasets of tunnel intensity images converted from point clouds obtained by a self-developed MLS system and compare its performance with other methods.

Classification of airborne laser scanning (ALS) point clouds is needed in digital cities and 3-D modeling. To efficiently recognize objects in ALS point clouds, we propose a novel hierarchical aggregated deep feature representation method, which can adequately employ spatial association of multilevel structures and deep feature discrimination. In our method, a 3-D deep learning model is constructed to represent the discriminative feature of each point cluster in a hierarchical structure by decreasing the within-class distance and increasing the between-class distance. Our method aggregates the discriminative deep features in different levels into a hierarchical aggregated deep feature that considers the spatial hierarchy and feature distinctiveness. Lastly, we build a multichannel 1-D convolutional neural network to classify the unknown points. Our tests demonstrate that the proposed hierarchical aggregated deep feature method can enhance point cloud classification results. Comparing with seven state-of-the-art methods, those results also verified the superior performance of our method.