International Journal of Pattern Recognition and Artificial Intelligence

Manual methods to detect and identify potential ground collapse hazards have a high volume of workload and rely on manual skills and experience strongly, with substantial inconsistencies. This paper proposes an automatic recognition method of potential underground hazards and designs an object detection-based algorithm to predict the hidden danger of ground collapse, called GCNet. The GCNet is a ground collapse hazard detection network, which is developed to detect underground hazards, including cavities, poor soil quality, pipelines, and soil background. The GCNet uses the deep residual network ResNet101 and feature pyramid network (FPN) to extract features and a task coordination network (TCN) to identify the category and location of hidden underground hazards. Further, a feature enhancement method based on the regional binary pattern is proposed to improve the accuracy of the proposed model by expanding the ground-penetrating radar (GPR) data by adding multi-level features to GPR images. The experiments are conducted on a large amount of real GPR data and experiment results show that the proposed automatic recognition method can surpass the existing deep learning-based methods in hidden underground hazard recognition and identification.

Abnormal cells in the human body that keep on mutating are termed to be cancer in medical terms. There are multiple types of cancer identified in human beings. It is very much essential to identify and classify the type of cancer in its earlier stage. This objective can be satisfied by artificial intelligence which has a subfield of machine learning to create a generalized model that could identify and classify cancer with increased performance. To perform the identification and classification of various cancer types, in this paper, two techniques are adopted. The optimized feature set computation was done using the Kernel-Induced Matriarch path tracking Elephant Herding Optimization (KIM-EHO) and the classification for the given samples was done using the Support Vector Machines (SVM). The proposed techniques are implemented with the benchmark datasets and the results proved that the proposed methodologies outperformed the existing methods in terms of accuracy, specificity, sensitivity and time complexity.

In RFID systems, many anti-collision algorithms, driven by the concept of rescheduling the response sequence between the reader and unidentified tags, have been put forward to solve tag collision problem, including ALOHA-based, tree-based and hybrid algorithms. In this paper, we propose a novel RFID anti-collision algorithm called EAQ-CBB, which adopts three main approaches: tag population estimation based on collided bit detection method, optimal partitions and trimmed query tree based on the strategy of collided block bit-mapping (QTCBB). The relatively accurate estimation of tag backlog and optimal partition ensure a great reduction of collisions in the initial phase. For each collided partition, a QTCBB process is introduced immediately, which eliminates all the empty slots and significantly reduces the collided slots. Simulation results show that EAQ-CBB performs good stability and scalability when the key parameters change. Compared with the existing algorithms, such as DFSA, QTI, T-GDFSA and CT, EAQ-CBB outperforms the others with high system throughput, low normalized latency and low normalized overhead at a low cost of energy, which makes it easier to be used widely in the efficient-aware and energy-aware applications.

The secret information hiding inside the cover image is termed image steganography, in which the secret information may be either in visual or text format. The concealment of secret information inside the cover image is devised by converting the information into the standard form using conventional image steganography. Here, the cover image is usually systematically altered to carry the secret binary bits after the translation of secret data into binary bits. The cover image may get distorted due to overload, making the hidden information obvious. As a result, the conventional image steganography approaches have a limited ability to conceal. Hence, this research introduces a novel image steganography using the optimized deep learning technique. For novel image steganography, an improved archerfish hunting optimization-based twin attention convolution capsule network (ImAho-TACCNet) is introduced for image steganography. Here, the proposed ImAho is utilized for modifying the tunable parameters of the TACCNet to enhance the efficiency of the image steganography process in terms of minimum mean square error (MSE) and maximal peak signal-to-noise ratio (PSNR). Besides, secret information compression and recursive encryption techniques further enhance the security of secret information. The analysis of ImAho-TACCNet based on various assessment measures like PSNR, SSIM and MSE accomplished enhanced outcomes with the values of 62.37, 0.9923, and 0.0165 for the hidden network model and 64.23, 0.9989, and 0.0125 for the extraction network model.

Semantic segmentation in indoor environments is a crucial task for artificial intelligence-driven visual robotics, enabling pixel-level classification results to facilitate robot path planning. Inspired by the success of multimodal models, we propose an end-to-end multimodal semantic segmentation model for image segmentation tasks in indoor scenes, which we call OIPNet. We design the OIP module to enhance the network’s ability to extract global information and enable information interaction in different directions. We have validated on NYUv2 and Sun RGB-D datasets, and the experiments show the generality and effectiveness of the proposed model. Our code is available at https://github.com/Mantee0810/OIP.

Aimed at the problems of complex industrial site environment, difficult identification of assembly features and low positioning accuracy, a new visual identification and positioning method is proposed, which can well identify and locate parts and products with different characteristics. First, combined filtering is used in preprocessing to repair low-quality images. Then, edge detection and contour finding are performed on the preprocessed image. Finally, the internal and external parameters of the camera are calculated through the camera calibration, and the acquired pixel coordinates are converted into world coordinates. The results show that the combined filtering algorithm has good noise reduction effect and can remove image surface noise, which improves the recognition accuracy for the identification and positioning of different feature parts by the subsequent contour search method, and the algorithm can meet the requirements of the actual positioning accuracy of the assembly.

With the advancement of smart mines, the need for gesture recognition for remote interaction between underground workers and machines has become crucial. However, traditional gesture recognition techniques require complex models that are very difficult to be deployed to the edge. To address this challenge, a gesture recognition method based on knowledge distillation is proposed in this study. First, the CSI ratio model is used to eliminate phase error and environmental noise, followed by the application of discrete wavelet transform to eliminate hardware noise interference. Then, the processed data is adaptively segmented using the principal component analysis and local anomaly factor algorithm to eliminate redundant static components. After that the processed CSI data is transformed into images using the relative position matrix method. Finally, knowledge distillation is employed to migrate knowledge from a teacher model to a student model, reducing the number of model parameters. Experiments conducted on the proposed method showed that it can achieve a recognition accuracy of 94.2% for hand gesture detection, which meets the requirement for gesture recognition in the mining industry.

Person identification using periocular images has emerged as a challenging scenario in efficient biometric analysis, particularly under less constrained environments. Accurate recognition is significant in rendering effective measures during the COVID-19 pandemic. In this research paper, the person identification process is performed based on a deep learning model. Several effectual methods have already been developed, but certain drawbacks still exist, like deteriorated image quality, high computational cost, increased error, less training ability, a requirement of high storage space and accuracy rate degradation. Hence, the proposed work introduces a Hybrid Optimal Dense Capsule network-based Periocular biometric system (HodCP) to conquer these demerits. The proposed work involves pre-processing, dimensionality reduction, hybrid feature extraction and Image matching. The pre-processing step is undertaken using Parabolic Contrast Enhancement (PCE) to balance the image contrast and enhance the image quality. Then the Two-Dimensional Principal Component Analysis (2D_PCA) is employed to minimize the image dimensionality. Deep features are extracted in the hybrid feature extraction process using Dense Convolutional-121 Capsule Network (DenseCapsNet). The net loss and hyperparameter tuning are performed through African Vultures Optimization (AVO) algorithm. Finally, image matching is performed using Weighted Distance Similarity (WDS), which identifies the similarity between the query image and a set of image samples based on the distance score. The simulation tool used for analyzing better performance is PYTHON. The data required to process the proposed work are collected from four benchmark datasets. The proposed work provides a better accuracy rate of CASIA-Iris-Mobile-V1.0 (99.01%), UBIPr (99.12%), Facemask detection dataset (98.67%) and Glasses versus without glasses dataset (98.83%), which is superior to the existing methods.

Retinal blood vessels can be obtained by image segmentation. This study proposes combining image enhancement and segmentation to obtain retinal blood vessels. The image enhancement stages use CLAHE and Denoised Non-Local Means to increase contrast and reduce noise on the original image, and Bottom-Hat (BTH) filtering is used to lighten dark features in the image so the features become lighter and darken the bright features in the image. Bottom Hat is applied to make the features of the blood vessels in the retinal image more visible. The segmentation architecture proposes BDDU-Net architecture which combines U-Net in the encoder part, DenseNet in the bridge part, and Bi-ConvLSTM in the decoder part. Image enhancement performance results are PSNR and SSIM. The PSNR is more than 40 dB on both the DRIVE and STARE datasets. The SSIM results are close to 1 on the DRIVE and STARE datasets. These results show that the image enhancement stages in the proposed method can enhance the quality of the original image. The segmentation performance results of BDDU-Net architecture are measured based on accuracy, sensitivity, specificity, IoU, and F1-Score. The DRIVE dataset obtained 95.578% for accuracy, 85.75% for sensitivity, 96.75% for specificity, 67.407% for IoU, and 80.53% for F1-Score. The STARE dataset obtained 97.63% for accuracy, 84.33% for sensitivity, 98.66% for specificity, 75.67% for IoU, and 86.15% for F1-Score. Based on the image enhancement and image segmentation results, these results show that the proposed method is great for enhancing image quality and excellent for blood vessel segmentation in retinal images, although IoU results on the DRIVE dataset need to be improved.

Traffic participant classification is crucial in autonomous driving perception. Millimeter wave radio detection and ranging radar is a cost-effective and powerful method to perform the task in adverse traffic scenarios, especially in bad inclement weather (e.g. fog, snow and rain) and poor lighting conditions. This paper presents an intelligent target classification algorithm for 77G radar based on correction data set. First, in order to handle the problem that the original data set may easily be interfered by obstacles, the angle information is filtered by analyzing the spatial information of radar signals, which means the interference clutter of obstacles can be effectively removed. Second, the primary data set is corrected using the significant difference between the micro-Doppler of the human body and car. Finally, the characteristic information of the radar signal is extracted, including distance, speed, orientation, micro-Doppler and reflection intensity, and the obtained data sets containing three types of targets (vehicles, human bodies and obstacles) are generated. The generated dynamic and static data sets are collected by sufficient experiments to construct deep learning classification models. The results show that the classification accuracy is improved by the measure of data set correction.

Ballistic missile attack-defense confrontation is a typical system-of-systems confrontation process. Ballistic missile early warning operations have the characteristics of high time-sensitivity, high complexity and high dynamic. It is impossible to deal with complex battlefield situations at the moment of decision. It is necessary to develop a detection plan in advance for possible battlefield situations, and intelligently recommend the optimal detection plan for the commander to make decisions based on the battlefield situation. The ballistic missile early warning detection plan is designed from three aspects: the design of search screen parameters, the determination of search data rate, and the allocation strategy of search resources in different airspace; Establish the evaluation indicator system and evaluation model of the ballistic missile early warning detection plan, evaluate and optimize multiple detection plans, and automatically recommend them to the commander according to the advantages and disadvantages of the operational effectiveness, connect the auxiliary decision-making chain of “design evaluation recommendation decision”, providing support for the intelligent decision-making of the ballistic missile early warning operation.