Efficient Passive Sensing Monocular Relative Depth Estimation

To take full advantage of the information of images captured by drones and given that most existing monocular depth estimation methods based on supervised learning require vast quantities of corresponding ground truth depth data for training, the model of unsupervised monocular depth estimation based on residual neural network of coarse–refined feature extractions for drone is therefore proposed. As a virtual camera is introduced through a deep residual convolution neural network based on coarse–refined feature extractions inspired by the principle of binocular depth estimation, the unsupervised monocular depth estimation has become an image reconstruction problem. To improve the performance of our model for monocular depth estimation, the following innovations are proposed. First, the pyramid processing for input image is proposed to build the topological relationship between the resolution of input image and the depth of input image, which can improve the sensitivity of depth information from a single image and reduce the impact of input image resolution on depth estimation. Second, the residual neural network of coarse–refined feature extractions for corresponding image reconstruction is designed to improve the accuracy of feature extraction and solve the contradiction between the calculation time and the numbers of network layers. In addition, to predict high detail output depth maps, the long skip connections between corresponding layers in the neural network of coarse feature extractions and deconvolution neural network of refined feature extractions are designed. Third, the loss of corresponding image reconstruction based on the structural similarity index (SSIM), the loss of approximate disparity smoothness and the loss of depth map are united as a novel training loss to better train our model. The experimental results show that our model has superior performance on the KITTI dataset composed by corresponding left view and right view and Make3D dataset composed by image and corresponding ground truth depth map compared to the state-of-the-art monocular depth estimation methods and basically meet the requirements for depth information of images captured by drones when our model is trained on KITTI.

Deployment of depth estimating models in computer vision applications like obstacle avoidance, scene reconstruction, and camera pose estimation has become a fundamental task in the present day. Most of the depth images generated by existing monocular depth estimation models have blurry approximations of the depth and resolution, especially in low-textured regions. However, the existing depth estimation models that provide better predictions of depth generally take multiple images of the same scene as an input for generating depth maps. This paper presents a transfer learning approach with densely connected convolutional neural networks that take only an RGB image as an input for deeper and high-quality prediction of depth. In the proposed solution, an encoder-decoder architecture is leveraged for extracting features from an RGB image and generating a depth map for that corresponding RGB image. The densely connected convolutional network with 161 layers (Densenet-161) is used as an encoder, and the decoder is made up of five upsampling blocks and one transposed convolutional layer. The evaluation results obtained after training the model on the benchmark NYU V2 depth dataset ( Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor Segmentation and Support Inference from RGBD Images. In: Proceedings of the IEEE Conference on Computer Vision, pp. 746-760(2012).) have shown that the proposed approach for monocular depth estimation with the encoder-decoder architecture of lesser complexity, trained with fewer parameters and iterations, performs better than the existing state-of-the-art techniques as the value of the average Root Mean Square Error (RMSE) between the predicted and ground-truth depths, calculated in the evaluation phase of the proposed approach, is 0.505 which is less than the RMSE values obtained in case of all the mentioned monocular depth estimation methods. Furthermore, the depth maps generated by the proposed model have a good quality resolution and have minimal effect on the surrounding conditions like the texture of the walls, illumination effects, etc.

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

1 - Congruence of deep learning in biomedical engineering: future prospects and challenges

Deep learning models are highly parameterized, resulting in difficulty in inference and transfer learning for image recognition tasks. In this work, we propose a layered pathway evolution method to compress a deep convolutional neural network (DCNN) for classification of masses in digital breast tomosynthesis (DBT). The objective is to prune the number of tunable parameters while preserving the classification accuracy. In the first stage transfer learning, 19 632 augmented regions-of-interest (ROIs) from 2454 mass lesions on mammograms were used to train a pre-trained DCNN on ImageNet. In the second stage transfer learning, the DCNN was used as a feature extractor followed by feature selection and random forest classification. The pathway evolution was performed using genetic algorithm in an iterative approach with tournament selection driven by count-preserving crossover and mutation. The second stage was trained with 9120 DBT ROIs from 228 mass lesions using leave-one-case-out cross-validation. The DCNN was reduced by 87% in the number of neurons, 34% in the number of parameters, and 95% in the number of multiply-and-add operations required in the convolutional layers. The test AUC on 89 mass lesions from 94 independent DBT cases before and after pruning were 0.88 and 0.90, respectively, and the difference was not statistically significant (p > 0.05). The proposed DCNN compression approach can reduce the number of required operations by 95% while maintaining the classification performance. The approach can be extended to other deep neural networks and imaging tasks where transfer learning is appropriate.

PurposeThe aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs.Materials and MethodsIn order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed.ResultsWhen dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects.ConclusionClinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.

Due to the major operating restrictions, ensuring security is the fundamental problem of Wireless Sensor Networks (WSNs). Because of their inadequate security mechanisms, WSNs are indeed a simple point for malware (worms, viruses, malicious code, etc.). According to the epidemic nature of worm propagation, it is critical to develop a worm defense mechanism in the network. This concept aims to establish novel malware detection in WSN that consists of several phases: “(i) Preprocessing, (ii) feature extraction, as well as (iii) detection”. At first, the input data is subjected for preprocessing phase. Then, the feature extraction takes place, in which principal component analysis (PCA), improved linear discriminant analysis (LDA), and autoencoder-based characteristics are retrieved. Moreover, the retrieved characteristics are subjected to the detection phase. The detection is performed employing combined shallow learning and DL. Further, the shallow learning includes decision tree (DT), logistic regression (LR), and Naive Bayes (NB); the deep learning (DL) includes deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Here, the DT output is given to the DNN, LR output is subjected to CNN, and the NB output is given to the RNN, respectively. Eventually, the DNN, CNN, and RNN outputs are averaged to generate a successful outcome. The combination can be thought of as an Ensemble classifier. The weight of the RNN is optimally tuned through the Self Improved Shark Smell Optimization with Opposition Learning (SISSOOL) model to improve detection precision and accuracy. Lastly, the outcomes of the suggested approach are computed in terms of different measures.

As the high voltage circuit breaker works, the metal parts are prone to many types of mechanical defects, such as plastic deformation, metal loss, corrosion, and so on. The defects will inevitably decrease the performance, mechanical reliability, lifetime, and other critical parameters of the breaker. Therefore, the detection of mechanical defects is of great help to reduce the mechanical failure rate of circuit breakers. In the present paper, a novel method based on edge detection and deep learning is proposed to detect the mechanical defects of the high voltage circuit breaker. Firstly, the high resolution images of the circuit breaker are photographed, and the components in the images are segmented in the minimum range by the contour detection algorithm. After binarization and morphological processing of the segmented images, the edge is drawn with the improved edge detection algorithm. However, the components of the circuit breaker are complicated, which results that the edge features of the critical component are difficult to extract. Then, the specific contour of components is detected by the depth estimation and segmentation methods. Secondly, a deep learning platform base on Tensorflow is established, which is an optimization method based on Convolutional Neural Network. In order to enhance the feature extraction capacity, a convolution kernel is inputted into a sparse self-coding network to proceeds with the optimal pre-training. Based on the above method, the features of images are automatically learned, and each convolution carries for the equilibrium of image entropy of convolution kernel by introducing similarity constraint rule. Finally, 3 types of mechanical defects, including plastic deformation, metal loss, corrosion, images and non-defect images as input are exploited to validate the CNN with convolution kernel optimized. The experimental results show that the average accuracy of the approach is better than the traditional convolution neural network model and has good feature extraction ability and generalization ability. In conclusion, combined with the improved edge detection and deep learning algorithms, the detection of typical mechanical defects can be well performed. For the collected images of circuit breaker, so as to reduce the impact of noise on defect accuracy of mechanical, the proposed method provides a reference for the non-destructive mechanical defect detection of high voltage circuit breaker.

The main purpose of the various methods of evaluating athlete feature recognition is to monitor the current health of the athletes, thereby providing some feedback on the quality of individual training. Based on deep learning and convolutional neural networks, this paper studies athlete target recognition and proposes a feature vector extraction method based on curvature zero point. Moreover, based on the ideas of deep learning and convolutional neural networks, this paper builds an athlete feature recognition model and optimizes the algorithm. In order to verify the feasibility and efficiency of feature extraction algorithm of the sport athletes proposed by this paper and to facilitate comparison with other algorithms, this paper conducts an algorithm performance test on the sport athlete database. The research results show that the method proposed in this paper has certain advantages in the feature extraction of athletes and can be used in subsequent sports training systems.

In many applications, such as robotic perception, scene understanding, augmented reality, 3D reconstruction, and medical image analysis, depth from images is a fundamentally ill-posed problem. The success of depth estimation models relies on assembling a suitably large and diverse training dataset and on the selection of appropriate loss functions. It is critical for researchers in this field to be made aware of the wide range of publicly available depth datasets along with the properties of various loss functions that have been applied to depth estimation. Selection of the right training data combined with appropriate loss functions will accelerate new research and enable better comparison with state-of-the-art. Accordingly, this work offers a comprehensive review of available depth datasets as well as the loss functions that are applied in this problem domain. These depth datasets are categorised into five primary categories based on their application, namely (i) people detection and action recognition, (ii) faces and facial pose, (iii) perception-based navigation (i.e., street signs, roads), (iv) object and scene recognition, and (v) medical applications. The important characteristics and properties of each depth dataset are described and compared. A mixing strategy for depth datasets is presented in order to generalise model results across different environments and use cases. Furthermore, depth estimation loss functions that can help with training deep learning depth estimation models across different datasets are discussed. State-of-the-art deep learning-based depth estimation methods evaluations are presented for three of the most popular datasets. Finally, a discussion about challenges and future research along with recommendations for building comprehensive depth datasets will be presented as to help researchers in the selection of appropriate datasets and loss functions for evaluating their results and algorithms.

Deep learning algorithms can be used to design systems that report data on patients and deliver warnings to medical applications or electronic health information if there are changes in the patient's health. These systems could be created using deep Learning. This helps verify that patients get the proper effective care at the proper time for each specific patient. A healthcare decision support system was presented using the Internet of Things and deep learning methods. In the proposed system, we examined the capability of integrating deep learning technology into automatic diagnosis and IoT capabilities for faster message exchange over the Internet. We have selected the suitable Neural Network structure (number of best-hidden layers and activation function classes) to construct the e-health system. In addition, the e-health system relied on data from doctors to understand the Neural Network. In the validation method, the total evaluation of the proposed healthcare system for diagnostics provides dependability under various patient conditions. Based on evaluation and simulation findings, a dual hidden layer of feed-forward NN and its neurons store the tanh function more effectively than other NN. To overcome challenges, this study will integrate artificial intelligence with IoT. This study aims to determine the NN's optimal layer counts and activation function variations.

Automated image classification is an essential task of the computer vision field. The tagging of images into a set of predefined groups is referred to as image classification. The implementation of computer vision to automate image classification would be beneficial because manual image evaluation and identification can be time-consuming, particularly when there are many images of different classes. Deep learning approaches are proven to overperform existing machine learning techniques in many fields in recent years, and computer vision is one of the most notable examples. The very deep neural network (VDCNN) is a powerful deep learning model for image classification, and this paper examines it briefly using MNIST handwritten digit dataset. This dataset is used to prove the efficacy of very deep neural networks over other deep learning models. The proposed study aims to comprehend the very deep neural network architecture used to accomplish a handwritten digit recognition task. The feasibility of the proposed model is evaluated using mean accuracy, validation accuracy, and standard deviation. The study results of the very deep neural network model are compared to a convolutional neural network and convolutional neural network with batch normalization. According to the results of the comparison study, very deep neural networks achieve high accuracy of 99.1% for a handwritten dataset. The outcome of the proposed work is used to interpret how well a very deep neural network performs when compared to the other two models of deep neural networks. This proposed architecture may be used to automate the classification of handwritten digits dataset.

Depth estimation from a single image is very challenging due to the inherent ambiguity of mapping a color image to a depth map. Previous work tackles this problem by exploiting various levels of features with multi-scale deep convolutional neural networks. However, most of the local geometric structure related monocular depth cues are lost when being propagated through convolutional neural network. Moreover, the error of depth cues related to local geometric structures is not considered in the loss function. In this work, we propose the GeoCueDepth convolutional neural network to exploit local geometric structure cues and propose a training loss that takes the geometric error into consideration, which significantly improve the performance of depth prediction in both accuracy and sharpness. Experiments show that the proposed method achieves 0.122 average relative error and 0.078 square relative error on the NYU Depth v2 data set, which outperforms state-of-the-art monocular depth estimation approaches.

The rapid proliferation of deepfake technology, driven by advanced generative models like GANs and Diffusion Models, has enabled the creation of hyper-realistic manipulated media that threatens information integrity, personal reputation, and national security. As these tools become more accessible, the need for automated, robust, and efficient detection systems is critical. Existing detection methodologies largely rely on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or Vision Transformers (ViTs). However, each approach faces significant limitations. CNN-based methods suffer from "temporal blindness," failing to detect inconsistencies across video frames. Hybrid CNN-RNN models address this but are hindered by slow training speeds and limited capacity for modeling long-range dependencies. While Transformer-based models excel at capturing global context, their quadratic computational complexity (O(N2)) makes them computationally expensive and memory-intensive, rendering them inefficient for processing long, high-resolution video sequences. Furthermore, many current models struggle to generalize effectively to unseen datasets and novel manipulation techniques. To overcome these challenges, this paper proposes a novel hybrid deepfake detection framework that integrates an EfficientNet backbone with the Mamba Selective State Space Model (SSM). This architecture leverages EfficientNet for robust spatial feature extraction and utilizes Mamba for temporal sequence modeling. Mamba introduces a content-aware selection mechanism and operates with linear-time complexity (O(N2)), allowing it to efficiently capture subtle long-range temporal inconsistencies that traditional models miss, without the heavy computational cost of Transformers. Experimental results demonstrate that this proposed system offers a scalable, efficient solution with superior generalization capabilities, making it highly suitable for real-time deepfake detection in diverse and complex video environments. Keywords: Deepfake Detection, GAN, Selective State Space Models (SSM), Mamba model, Vision Mamba (Vim), Temporal Sequence Modeling, EfficientNet Backbone, machine learning, deep learning, convolutional neural networks, transformers, attention mechanisms, benchmarking systems, datasets, Synthetic Media Detection, Linear-Time Complexity, Video Anomaly Detection, Computational Efficiency, Hybrid CNN-SSM, Facial Forgery Detection, Long-Range Dependency Modeling.

Over the past few decades, research has focused heavily on automatic speech recognition (ASR). Although ASR for a few languages is close to reality; ASR for the low resource languages like Malayalam is still in its infancy. Here, in this work, the authors discuss the experiment conducted on accented data of Malayalam speech using two approaches. One approach uses the spectral features for modeling using deep convolutional neural network and the other uses the influential features of speech signals for modeling using LSTM-RNN approach. The proposed methodology comprises three distinct stages; dataset preparation, feature extraction, classification, and hence the construction of deep learning models that recognize the accent-based spoken sentences in the Malayalam language. Mel-frequency cepstral coefficient (MFCC) algorithm, short-term Fourier transform (STFT), and mel spectrogram methodologies are used for feature engineering and hence the features that represent the speech signals are used for constructing the accented ASR system for the Malayalam language using long short-term memory (LSTM) a recurrent neural network (RNN). The spectrogram dataset has been constructed for the speech dataset and used for constructing the ASR model with deep convolutional neural network (DCNN). The result of the experiment shows that LSTM-based RNN outperforms DCNN for the proposed dataset that has been constructed for the experiment in the natural recording environment.