Deep Learning For Image Recognition Research Articles

Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring

IntroductionPotatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional disease monitoring methods rely on manual visual inspection, which is inefficient and prone to subjective bias. The application of deep learning in image recognition has led to object detection models such as YOLO (You Only Look Once), which have shown high efficiency in disease identification. However, complex climatic conditions in real agricultural environments challenge model robustness, and current mainstream models struggle with accurate recognition of the same diseases across different plant species.MethodsThis paper proposes the SIS-YOLOv8 model, which enhances adaptability to complex agricultural climates by improving the YOLOv8 network structure. The research introduces three key modules: 1) a Fusion-Inception Conv module to improve feature extraction against complex backgrounds like rain and haze; 2) a C2f-SIS module incorporating Style Randomization to enhance generalization ability for different crop diseases and extract more detailed disease features; and 3) an SPPF-IS module to boost model robustness through feature fusion. To reduce the model’s parameter size, this study employs the Dep Graph pruning method, significantly decreasing parameter volume by 19.9% and computational load while maintaining accuracy.ResultsExperimental results show that the SIS-YOLOv8 model outperforms the original YOLOv8n model in disease detection tasks for potatoes and tomatoes, with improvements of 8.2% in accuracy, 4% in recall rate, 5.9% in mAP50, and 6.3% in mAP50-95.DiscussionThrough these network structure optimizations, the SIS-YOLOv8 model demonstrates enhanced adaptability to complex agricultural environments, offering an effective solution for automatic crop disease detection. By improving model efficiency and robustness, our approach not only advances agricultural disease monitoring but also contributes to the broader adoption of AI-driven solutions for sustainable crop management in diverse climates.

Research on image recognition and processing application technology of unmanned vehicle based on deep learning

Abstract. Image recognition technology is critically important in various fields, including the rapidly advancing sector of autonomous vehicles. As one of the core components enabling driverless cars to perceive their environment and make informed decisions, image recognition has seen significant advancements due to deep learning. This paper focuses on the application of deep learning in image recognition for self-driving cars and explores its implications for the future of autonomous driving technology. To begin with, this paper examines the empirical evaluation of deep learning models in highway driving scenarios. By employing Convolutional Neural Networks (CNN), these models achieve high detection rates and superior accuracy in recognizing vehicles and lanes. The robustness of these models is tested under varying weather conditions and times, demonstrating their effectiveness compared to classical computer vision techniques. Next, the paper discusses radar-camera fusion technology, highlighting different fusion strategies such as data-level, feature-level, object-level, and hybrid-level. The findings suggest that while feature-level fusion excels in detecting small objects in complex scenes, hybrid-level fusion is optimal for diverse driving situations. This section provides valuable insights into the integration of multimodal data for improved object recognition and semantic segmentation. After discussing fusion technologies, the paper finally reviews the security challenges posed by adversarial attacks on deep learning-based unmanned systems.

Deep neural networks for automated damage classification in image-based visual data of reinforced concrete structures

Significant strides in deep learning for image recognition have expanded the potential of visual data in assessing damage to reinforced concrete (RC) structures. Our study proposes an automated technique, merging convolutional neural networks (CNNs) and fully convolutional networks (FCNs), to detect, classify, and segment building damage. These deep networks extract RC damage-related features from high-resolution smartphone images (3264 × 2448 pixels), categorized into two groups: damage (exposed reinforcement and spalled concrete) and undamaged area. With a labeled dataset of 2000 images, fine-tuning of network architecture and hyperparameters ensures effective training and testing. Remarkably, we achieve 98.75 % accuracy in damage classification and 95.98 % in segmentation, without overfitting. Both CNNs and FCNs play crucial roles in extracting features, showcasing the adaptability of deep learning. Our promising results validate the potential of these techniques for inspectors, providing an effective means to assess the severity of identified damage in image-based evaluations.

Research on Optimization of Image Recognition Algorithm Based on Deep Learning

With the rapid development of information technology, image recognition has become increasingly crucial in numerous fields. Deep learning, as a highly potent machine learning approach, has brought about a revolutionary breakthrough in image recognition. This paper delves into the optimization of image recognition algorithms based on deep learning. Firstly, it analyzes the application status of deep learning in image recognition, covering common neural network architectures such as convolutional neural network (CNN). Then, the optimization strategy of the image recognition algorithm is elaborated from multiple aspects, including data augmentation to increase data diversity, model structure optimization for better feature extraction, and hyperparameter adjustment to enhance performance. Through extensive experiments, the effects of different optimization methods are compared meticulously. It is demonstrated that the optimized algorithm shows significant improvements in accuracy, robustness, and efficiency. Finally, the paper looks ahead to the future development trend of image recognition algorithms based on deep learning, envisioning further advancements and broader applications.

Classification of pain expression images in elderly with hip fractures based on improved ResNet50 network.

The aim of this study is designed an improved ResNet 50 network to achieve automatic classification model for pain expressions by elderly patients with hip fractures. This study built a dataset by combining the advantages of deep learning in image recognition, using a hybrid of the Multi-Task Cascaded Convolutional Neural Networks (MTCNN). Based on ResNet50 network framework utilized transfer learning to implement model function. This study performed the hyperparameters by Bayesian optimization in the learning process. This study calculated intraclass correlation between visual analog scale scores provided by clinicians independently and those provided by pain expression evaluation assistant(PEEA). The automatic pain expression recognition model in elderly patients with hip fractures, which constructed using the algorithm. The accuracy achieved 99.6% on the training set, 98.7% on the validation set, and 98.2% on the test set. The substantial kappa coefficient of 0.683 confirmed the efficacy of PEEA in clinic. This study demonstrates that the improved ResNet50 network can be used to construct an automatic pain expression recognition model for elderly patients with hip fractures, which has higher accuracy.

Sign Language Recognition

Abstract: Communication through signs has consistently been a significant way for communication among hearing and speech impaired humans, generally called deaf and dumb. It is the only mode of communicating for such individuals to pass on their messages to other human beings, and hence other humans need to comprehend their language. In this project, a sign language detection or recognition web framework is proposed with the help of image processing. This application would help in recognizing Sign Language. The dataset used is the Sign Language dataset. This application could be used in schools or any place, which would make the communication process easier between the impaired and non-impaired people. The proposed method can be used for the ease of recognition of sign language. The method used is Deep Learning for image recognition and the data is trained using Random Forest. Using this method, we would recognize the gesture and predict which sign is shown

Research on Intelligent System of Multimodal Deep Learning in Image Recognition

In this paper, a multi-scale image estimation method based on wavelet transform is proposed, which can effectively remove motion features from multiple videos. Then the autoencoder with sparsity limit is used to adjust the input signal to achieve effective compression. The effective features are extracted and the optimal unique vector is learned. The improved convolutional neural network is used to recognize weak moving objects. Experiments show that the algorithm can achieve high accuracy without large-scale learning samples, and the highest recognition rate is 99.36%. This algorithm has a great improvement over conventional algorithm.

Developing a plogging activity tracking app using deep learning for image recognition

Plogging is an activity that combines jogging with picking up litter, and participants often share their efforts on social media. However, the repetitive bending involved in plogging may cause back strain, and manually entering details such as the location and quantity of litter could slow the spread of this activity. This study sought to create and test a deep learning application to automatically monitor and record plogging by identifying the type and quantity of litter. We employed Convolutional Neural Networks (CNN) and YOLOv5 to develop an image recognition model. This model allowed users to easily log their plogging efforts by simply taking a photograph, removing the need to manually input the litter details. Moreover, we proposed a reward system that uses the collected trash amount and the distance covered to promote competition among users. We developed the first application that uses deep learning to automatically identify litter for tracking plogging activities. However, as this application was only a prototype, no comparative studies or usability tests were done. In future research, we plan to assess the application's usability and compare it with other similar applications.

Performance of convolutional neural networks on CIFAR and Dog Breed classification dataset

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 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.

AI climate tipping‐point discovery (ACTD)

AI climate tipping‐point discovery (ACTD)

Application of Deep Learning in Image Recognition of Citrus Pests

The occurrence of pests at high frequencies has been identified as a major cause of reduced citrus yields, and early detection and prevention are of great significance to pest control. At present, studies related to citrus pest identification using deep learning suffer from unbalanced sample sizes between data set classes, which may cause slow convergence of network models and low identification accuracy. To address the above problems, this study built a dataset including 5182 pest images in 14 categories. Firstly, we expanded the dataset to 21,000 images by using the Attentive Recurrent Generative Adversarial Network (AR-GAN) data augmentation technique, then we built Visual Geometry Group Network (VGG), Residual Neural Network (ResNet) and MobileNet citrus pest recognition models by using transfer learning, and finally, we introduced an appropriate attention mechanism according to the model characteristics to enhance the ability of the three models to operate effectively in complex, real environments with greater emphasis placed on incorporating the deep features of the pests themselves. The results showed that the average recognition accuracy of the three models reached 93.65%, the average precision reached 93.82%, the average recall reached 93.65%, and the average F1-score reached 93.62%. The integrated application of data augmentation, transfer learning and attention mechanisms in the research can significantly enhance the model’s ability to classify citrus pests while saving training cost and time, which can be a reference for researchers in the industry or other fields.

Research and application of deep learning in image recognition

In recent years, deep learning technology has been one of the most important representatives of progress in the field of artificial intelligence technology in China. It has made great achievements in many fields, such as language recognition, natural language processing, image processing and video analysis. This paper further discusses the application research and practice of deep learning technology in modern digital image recognition. Deep learning technology has played a more and more important role in modern digital image recognition.

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image, retaining useful information to the greatest extent, and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area. Finally, terminal area of Guangzhou Airport is selected as the research object, the method proposed in this article is used to classify historical weather data in similar scenes, and the performance is compared with other state-of-the-art methods. The experimental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather; at the same time, compared with the actual flight volume in the Guangzhou terminal area, IDCEC's recognition results of similar weather scenes are consistent with the recognition of experts in the field.

A Deep Learning-Based Encrypted VPN Traffic Classification Method Using Packet Block Image

Network traffic classification has great significance for network security, network management and other fields. However, in recent years, the use of VPN and TLS encryption had presented network traffic classification with new challenges. Due to the great performances of deep learning in image recognition, many solutions have focused on the deep learning-based method and achieved positive results. A traffic classification method based on deep learning is provided in this paper, where the concept of Packet Block is proposed, which is the aggregation of continuous packets in the same direction. The features of Packet Block are extracted from network traffic, and then transformed into images. Finally, convolutional neural networks are used to identify the application type of network traffic. The experiment is conducted using captured OpenVPN dataset and public ISCX-Tor dataset. The results shows that the accuracy is 97.20% in OpenVPN dataset and 93.31% in ISCX-Tor dataset, which is higher than the state-of-the-art methods. This suggests that our approach has the ability to meet the challenges of VPN and TLS encryption.

Adversarial attacks and defenses in deep learning for image recognition: A survey

In recent years, researches on adversarial attacks and defense mechanisms have obtained much attention. It’s observed that adversarial examples crafted with small malicious perturbations would mislead the deep neural network (DNN) model to output wrong prediction results. These small perturbations are imperceptible to humans. The existence of adversarial examples poses great threat to the robustness of DNN-based models. It is necessary to study the principles behind it and develop their countermeasures. This paper surveys and summarizes the recent advances in attack and defense methods extensively and in detail, analyzes and compares the pros and cons of various attack and defense schemes. Finally we discuss the main challenges and future research directions in this field.

Summary of Research on Application of Deep Learning in Image Recognition

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.

Characterizing Malignant Melanoma Clinically Resembling Seborrheic Keratosis Using Deep Knowledge Transfer.

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.

Improved Capsule Endoscopy Using New Computer Vision Technologies

Advanced computer vision technologies can be used to improve the performance of capsule endoscopy. Specifically, new technologies can be used to enhance the image quality of capsule endoscopes, which has previously been limited by mechanical constraints. The trajectory of a capsule endoscope, which can be estimated by reconstructing the captured images, helps to predict the location of lesions. Computer vision technology is utilized in a more practical way in a recently developed capsule endoscope comprising a stereo camera. Using this device, images can be interpreted in three dimensions, enabling more accurate measurements of size. The use of deep learning for image recognition is associated with an improved lesion detection rate and reduced reading time in clinicians. This evolving technology requires close cooperation between medical and IT professionals for optimized clinical use.

Application of deep learning in image recognition and diagnosis of gastric cancer

Application of deep learning in image recognition and diagnosis of gastric cancer