Intelligent Crater Detection on Planetary Surface Using Convolutional Neural Network

With the rapid development of technology, artificial intelligence is gradually applied in the field of image processing. Traditional image processing and recognition takes human extraction of features as a means to describe and characterize low-level edge information in images, but a large number of features extraction causes certain troubles to image recognition, and it is difficult to extract deep-level information. In recent years, deep learning, as an important emerging technology in the field of image recognition, has broad application prospects, and the research on image recognition technology has important theoretical value and practical significance to promote the development of computer vision and artificial intelligence. It opens up a whole new direction in image processing research, thus meeting the demand for efficient image processing. This paper reviews the applications of deep learning in image recognition, analyzes the classical convolutional neural network model, and summarizes the applications of deep learning in the fields of license plate recognition, face recognition, and medical image recognition.

Deep learning is a new field in machine learning, which is based on the neural network model to extract features by simulating human brain cognition. Image recognition is an essential subject in the field of deep learning and has made significant progress in recent years. In the training process, only the computer needs to cooperate, and no personnel need to participate in the process to get a good image recognition effect. This paper makes an in-depth analysis on the application of deep learning in image recognition, discusses the necessary steps of image trial and the basic principle of the neural network, and puts forward some problems and popular deep learning models used in image recognition.

With the rapid development of high-tech in China, the field of artificial intelligence has also been promoted. Image recognition is an important topic in the field of artificial intelligence, which mainly includes two modules: classification recognition and feature extraction; At the same time, as an important research direction of AI, deep learning has made rapid progress in recent years. It has been widely used in image recognition, speech recognition and other fields and has achieved great success. This paper analyzes the application of deep learning in image recognition, mainly from the aspects of face recognition, remote sensing image classification, etc. Its purpose is to provide help for relevant practitioners, so as to promote the development of image recognition in the tide of AI development.

This research aims to study the deep learning applications in image recognition and classification tasks. The advantages and limitations of deep learning are explored by analysing existing deep learning algorithms and their applications on tasks such as using images to do classification, image segmentation, and target detection. In the experimental part, this paper evaluates the performance of deep learning in image recognition by using classical neural network models, including training and testing models on a large size of image datasets. The results show that neural network has good classification and detection capabilities in image recognition tasks and also achieves good results for image segmentation tasks. However, the model training process of shallow neural network models is time-consuming and performs poorly for small-scale datasets. Based on these situations, this paper proposes some optimization strategies to achieve high performance and efficiency of neural networks in image recognition and analyze the performance differences between different strategies .

Deep learning has been the core topic in machine learning and convolutional neural network is one of the most prominent approaches. Convolutional neural network has won numerous competitions in recent years. It has outstanding results in image recognition. We review the different deep learning approaches which have been used in the field of image classification and localization.

Big data technology has not only become an important driving force of industry digitalization and intelligence, but also a fundamental source of innovation and development. Through the study of pricing model, the application of big data technology in service trading can be more standardized and orderly. As a special data form in the recommendation field, traditional recommendation methods are often difficult to model session sequence data. Although some scholars have improved the traditional recommendation method to solve the problem of session sequence modeling, the effectiveness of the recommendation system is limited by many problems in the model structure. In recent years, with deep learning in image recognition, natural language processing, speech recognition and other breakthroughs in the field of artificial intelligence, will study deep into the recommendation algorithm, can help to effectively solve the traditional recommendation algorithm of multi-source heterogeneous information, data sparse, cold start, data characteristics of the design of artificial rely on such issues, in this case, The results of traditional recommendation methods are often inaccurate, and the results are often lagging and repeatable.

Simple SummaryMalignant melanomas (MMs) with aypical clinical presentation constitute a diagnostic pitfall, and false negatives carry the risk of a diagnostic delay and improper disease management. Among the most common, challenging presentation forms of MMs are those that clinically resemble seborrheic keratosis (SK). On the other hand, SK may mimic melanoma, producing ‘false positive overdiagnosis’ and leading to needless excisions. The evolving efficiency of deep learning algorithms in image recognition and the availability of large image databases have accelerated the development of advanced computer-aided systems for melanoma detection. In the present study, we used image data from the International Skin Image Collaboration archive to explore the capacity of deep knowledge transfer in the challenging diagnostic task of the atypical skin tumors of MM and SK.Malignant melanomas resembling seborrheic keratosis (SK-like MMs) are atypical, challenging to diagnose melanoma cases that carry the risk of delayed diagnosis and inadequate treatment. On the other hand, SK may mimic melanoma, producing a ‘false positive’ with unnecessary lesion excisions. The present study proposes a computer-based approach using dermoscopy images for the characterization of SΚ-like MMs. Dermoscopic images were retrieved from the International Skin Imaging Collaboration archive. Exploiting image embeddings from pretrained convolutional network VGG16, we trained a support vector machine (SVM) classification model on a data set of 667 images. SVM optimal hyperparameter selection was carried out using the Bayesian optimization method. The classifier was tested on an independent data set of 311 images with atypical appearance: MMs had an absence of pigmented network and had an existence of milia-like cysts. SK lacked milia-like cysts and had a pigmented network. Atypical MMs were characterized with a sensitivity and specificity of 78.6% and 84.5%, respectively. The advent of deep learning in image recognition has attracted the interest of computer science towards improved skin lesion diagnosis. Open-source, public access archives of skin images empower further the implementation and validation of computer-based systems that might contribute significantly to complex clinical diagnostic problems such as the characterization of SK-like MMs.

This paper researches the localization method based on the finer-grained channel parameters using a deep learning model with a single access point. The basic principle is to transform the localization problem into a classification problem by using the advantages of deep learning in image recognition. First, we use the 2D recursively applied and projected-multiple signal classification algorithm to estimate the path parameters, and then the signal spectrum in 2D space is obtained which is plotted in a image. Then we present a structure based on convolutional neural network to obtain the local signal features and learn the correlation between its two-dimensional parameters and the coordinates of the target. Finally, we use the commercial-grade ray tracing software to evaluate our proposed methods, and the simulation results show that the proposed method can locate the target accurately using only a single access point.

Race against the Machine: can deep learning recognize microstructures as well as the trained human eye?

Комп’ютерне моделювання глибинного навчання для розпізнавання зображень

Remote sensing images contain a wealth of geospatial information. To accurately identify different geospatial categories and extract relevant data, image semantic segmentation plays a crucial role. In recent years, deep learning technology has brought significant breakthroughs to semantic segmentation of remote sensing images, significantly enhancing its performance. This paper investigates the application of deep learning technologies in remote sensing image semantic segmentation, based on Convolutional Neural Networks (CNN) and Transformer-based semantic segmentation methods. It conducts an in-depth comparison of their structural characteristics and applicable scenarios, summarizes the achievements and shortcomings of existing research, and provides technical references and theoretical support for future studies, thereby contributing to the further development of deep learning technology in the field of remote sensing. Research results indicate that CNN-based semantic segmentation methods still hold advantages in extracting local features and achieving efficient segmentation, whereas Transformers address CNN's limitations in global context modeling and long-range dependency capture. Therefore, the collaborative integration of CNN and Transformers will become an important research direction for enhancing model performance in the future.

This study aims to enhance object detection systems by comparing pre-trained classification models with custom-trained ones, focusing on task-based deep learning for image recognition. The problem addressed is the challenge of accurately detecting and classifying objects in complex environments where traditional recognition systems may fall short. The proposed solution leverages transfer learning utilizing pre-trained models like ResNet or VGGNet as feature extractors. By exploiting the convolutional layers of these models, the system captures common features for specific detection tasks. Experimental analyses on benchmark datasets confirm the efficacy of this approach, demonstrating improved detection accuracy and efficiency in various scenarios. Specifically, FasterRCNN achieves a mean Average Precision (mAP) of 78% on synthetic datasets and 74% on real datasets at an Intersection over Union (IoU) threshold of 0.5. This indicates FasterRCNN's superior performance in terms of accuracy, making it a strong candidate for applications requiring high detection accuracy.

Deep learning is a complex machine learning algorithm that can learn the deep inner rules of a sample. In recent years, deep learning has achieved good results in the field of image processing. The essence of deep learning is to build a multi-layered machine learning architecture that trains on large-scale data. With the development of computer technology, deep learning has achieved good results in image recognition and natural language processing. Deep learning is also becoming more and more widely used in industry. This paper introduces the applications of deep learning in the industrial field, and finally discusses development and challenges of deep learning in industrial applications in the future.

Deep convolutional neural networks have achieved great success in recent years. They have been widely used in various applications such as optical and SAR image scene classification, object detection and recognition, semantic segmentation, and change detection. However, deep neural networks rely on large-scale high-quality training data, and can only guarantee good performance when the training and test data are independently sampled from the same distribution. Deep convolutional neural networks are found to be vulnerable to subtle adversarial perturbations. This adversarial vulnerability prevents the deployment of deep neural networks in security-sensitive applications such as medical, surveillance, autonomous driving and military scenarios. This paper first presents a holistic view of security issues for deep convolutional neural network-based image recognition systems. The entire information processing chain is analyzed regarding safety and security risks. In particular, poisoning attacks and evasion attacks on deep convolutional neural networks are analyzed in detail. The root causes of adversarial vulnerabilities of deep recognition models are also discussed. Then, we give a formal definition of adversarial robustness and present a comprehensive review of adversarial attacks, adversarial defense, and adversarial robustness evaluation. Rather than listing existing research, we focus on the threat models for the adversarial attack and defense arms race. We perform a detailed analysis of several representative adversarial attacks on SAR image recognition models and provide an example of adversarial robustness evaluation. Finally, several open questions are discussed regarding recent research progress from our workgroup. This paper can be further used as a reference to develop more robust deep neural network-based image recognition models in dynamic adversarial scenarios.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?