Objects In Images Research Articles

Clustering-based interactive image segmentation

Objectives. The task of color image segmentation without the use of preliminary training is considered. It arises, for example, when it is necessary to perform image segmentation with semantic and color properties unknown in advance immediately after their acquisition, or when the set of images intended for segmentation is too small, as well as when performing preliminary "exploratory" analysis of images. In such cases, powerful neural network and other segmentation tools that require deep learning can not be used.Methods. An algorithm for interactive image segmentation is proposed, based on the analysis of the colors of areas selected interactively. First, in interactive mode, the image areas belonging to the objects are selected very approximately, and then regions belonging to the background are chosen. In the next step, the set of colors of the selected object areas and the set of colors of the selected background areas are clustered separately by one of the clustering algorithms, for example, k-means, fuzzy c-means, or the multi-level clustering algorithm proposed by the author. After this, non-informative elements are removed from the set of cluster centers describing the objects and the set of clusters presenting the background. The modified sets of object and background cluster centers are used for image segmentation.Results . The constructed algorithm allows selection of the required objects in color images if the colors of the objects and the background are different. Interactive selection of object areas and background areas does not require accuracy or much effort and usually takes several tens of seconds. For selection, rectangular areas that lie entirely inside the object images, and rectangular areas that belong completely to the background can be used. Below an example of interactive regions selection and color image segmentation is shown.Conclusion. The experiments performed showed the effectiveness of the proposed approach to segmenting color images. It can be used in cases where the semantic and color properties of images are not known in advance, and in cases where the use of more powerful deep learning methods, including neural networks, is too expensive or impossible.

Development of an algorithm for compressing aerospace images for the subsequent recognition and identification of various objects

The object of study is the recognition and identification of various objects in aerospace images. To solve the problems of compressing hyperspectral aerospace images with losses, the development of a compression algorithm is proposed. As a result, an algorithm has been developed for compressing aerospace images for subsequent recognition and identification of various objects using wavelet transform for processing high- and medium-resolution space images when monitoring from remote sensing satellites, based on the use of structural features of object images. In particular, orthogonal and wavelet transforms are presented, adapted for compression of hyperspectral aerospace images with losses, an adaptive discrete cosine transform algorithm is presented, followed by quantization with a loss level and compression. Thanks to a series of experiments on hyperspectral aerospace images, the effectiveness of the proposed algorithm in terms of the degree of compression, as well as the characteristics of the limits of its applicability, can be highlighted. The use of wavelets provides progressive compression of the bitstream, which makes it possible to achieve lossless compression with minimal loss of information due to the modified Huffman algorithm with a compression ratio of 9 more than 2.5 times in existing algorithms, as well as the quality metric of the restored images, the peak signal-to-noise ratio is sufficiently below 32.56. The developed compression algorithm demonstrates the effectiveness of its application in terms of a set of characteristics and is superior to analogues. The scope and conditions for the practical use of the results obtained is a comparison of the proposed algorithm with the results of experiments obtained for universal compression algorithms for archivers and a compressor

EFE-CNA Net: An Approach for Effective Image Deblurring Using an Edge-Sensitive Focusing Encoder

Deep learning-based image deblurring techniques have made great advancements, improving both processing speed and deblurring efficacy. However, existing methods still face challenges when dealing with complex blur types and the semantic understanding of images. The segment anything model (SAM), a versatile deep learning model that accurately and efficiently segments objects in images, facilitates various tasks in computer vision. This article leverages SAM’s proficiency in capturing object edges and enhancing image content comprehension to improve image deblurring. We introduce the edge-sensitive focusing encoder (EFE) module, which utilizes masks generated by the SAM framework and re-weights the masked portion following SAM segmentation by detecting its features and high-frequency information. The EFE module uses the masks to locate the position of the blur in an image while identifying the intensity of the blur, allowing the model to focus more accurately on specific features. Masks with greater high-frequency information are assigned higher weights, prompting the network to prioritize them during processing. Based on the EFE module, we develop a deblurring network called the edge-sensitive focusing encoder-based convolution–normalization and attention network (EFE-CNA Net), which utilizes the EFE module to enhance the deblurring process, employs an image-mask decoder to merge features from both the image and the mask from the EFE module, and incorporates the CNA Net as its base network. This design enables the model to focus on distinct features at various locations, enhancing its learning process through the guidance provided by the EFE module and the blurred images. Testing results on the RealBlur and REDS datasets demonstrate the effectiveness of the EFE-CNA Net, achieving peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics of 28.77, 0.902 (RealBlur-J), 36.40, 0.956 (RealBlur-R), 31.45, and 0.919 (REDS).

Discrete Cosine Transform Algorithm Analysis of The Application of QR Codes on Birth Certificate Image Objects

Security and authentication of important documents such as birth certificates have become a major concern today and in the digital age. In this context, the use of watermarking technology is an effective solution to add additional information to digital documents without destroying their authenticity. The DCT method is used to convert images into the frequency domain, allowing the watermark to be added without disturbing the appearance of the document. Testing ensures that the watermarking process does not damage the appearance of the original document and that the watermark information can be accurately re-extracted. The research results show that the QR code watermark using the DCT method successfully provides secure and authentic birth certificates. The application of the Discrete Cosine Transform (DCT) method to the birth certificate QR code watermarking process was able to significantly improve document security and authentication without compromising document quality or important information. This thesis outlines the extensive steps in the practical application of the QR code watermark on birth certificates. From data preparation including birth certificate images and QR codes to the watermark conversion and extraction process, all steps are explained in detail. Thus, applying this watermarking technique can be an effective solution to protect the integrity of birth certificates and similar documents in the digital environment.

DASOD: Detail-aware salient object detection

Salient object detection (SOD) is a challenging task in computer vision. Current SOD approaches have made significant progress, but they fail in challenging scenarios. This paper categorizes the existing challenges in SOD into four groups: images with complex backgrounds, low contrast, transparent objects, and occluded objects. Then, the Detail-Aware Salient Object Detection (DASOD) method is proposed to address these challenging scenarios. To the best of our knowledge, DASOD is the first method that considers mentioned challenging situations together and detects salient objects in images through camouflaged object detection (COD). DASOD has two main stages: 1) pseudo-mask generation and 2) refinement. It first generates a pseudo-mask using the body label and super-resolution technique, then refines the pseudo-mask with the detail map produced by the pseudo-edge generator to detect salient objects with clear boundaries in the pseudo-mask refinement module. This module quantifies uncertainty using the conditional normalizing flows (cFlow) based conditional variational auto-encoder (cVAE) to generate reliable results. Extensive experiments are conducted on six datasets, and the performance of DASOD is compared with 18 state-of-the-art methods. The results demonstrate that DASOD outperforms its competitors and can accurately detect the salient objects when the image background is cluttered and the contrast between foreground and background is low. Also, it effectively detects the transparent and occluded objects in images. It achieves MAE rates of 0.052, 0.033, 0.027, 0.024, 0.059, and 0.088 on DUT-OMRON, DUTS-TE, ECSSD, HKU-IS, PASCAL-S, and SCAS datasets, respectively. All the implementation source codes and results are available at: https://github.com/BaharehAsheghi/DASOD.

Effective Algorithm for Biomedical Image Segmentation

Biomedical image segmentation plays an important role in quantitative analysis, clinical diagnosis, and medical manipulation. Objects in medical images have different scales, types, complex backgrounds, and similar tissue appearances, making information extraction challenging. To solve this problem, a module is proposed that takes into account the features of images, which will improve the biomedical image segmentation network FE-Net. An integral part of the FE-Net algorithm is the connection skipping mechanism, which ensures the connection and fusion of feature maps from different layers in the encoder and decoder. Features at the encoder level are combined with high-level semantic knowledge at the decoder level. The algorithm establishes connections between feature maps, which is used in medicine for image processing. The proposed method is tested on three public datasets: Kvasir-SEG, CVC-ClinicDB and 2018 Data Science Bowl. Based on the results of the study, it was found that FE-Net demonstrates better performance compared to other methods in terms of Intersection over Union and F1-score. The network under consideration copes more effectively with segmentation details and object boundaries, while maintaining high accuracy. The study was conducted jointly with the Department of Magnetic Resonance Imaging of the N. N. Alexandrov National Oncology Center. Access to the source code of the algorithm and additional technical details is available at https://github.com/tyjcbzd/FE-Net.

Evaluating the Effectiveness of Panoptic Segmentation Through Comparative Analysis

Image segmentation method is extensively used in the fields of computer vision, machine learning, and artificial intelligence. The task of segmentation is to distinguish objects in images either by their boundaries or as entire objects from the entire image. Image segmentation methods are implemented as instance, semantic, and panoptic segmentation. In this article, the panoptic segmentation method, seen as an advanced stage of instance and semantic segmentation, has been applied to three datasets and compared with the instance segmentation method. Experimental results are presented visually. Numerical results have been analyzed with the Panoptic Quality (PQ) and Semantic Quality (SQ) metrics. It has been observed that the segmentation outcome was best for the CityScapes dataset for panoptic segmentation.

Radiological image analysis using effective channel extension and fusion network based on COVID CT images

Attention mechanisms demonstrate significant capabilities in improving the performance of COVID CT image classification networks. Existing strategies often employ global pooling to acquire feature vectors, overlooking local feature information. Complex attention modules are then applied to enhance network performance, inevitably leading to increased model complexity. To address these issues, this paper introduces an Effective Channel Expansion and Fusion (ECEF) module specifically designed to enhance the performance of medical COVID CT image classification. The module represents a simple yet effective convolutional neural network module. In the channel direction of the given feature map, the module performs segmentation, conducts global pooling operations separately on the segmented and overall feature maps to obtain corresponding attention vectors, and merges them to produce high-dimensional attention vectors. Channel attention feature vectors are derived along the two pooling operation directions and dynamically fused. The resulting attention vectors are multiplied by the input feature map for adaptive feature refinement. Through these operations, the ECEF module adeptly captures local features in COVID CT images, enhancing the classification performance of medical COVID CT images. As a lightweight and versatile module, ECEF seamlessly integrates into any CNN architecture with negligible overhead. It can undergo end-to-end training alongside basic CNNs. ResNet is selected as the backbone network for empirical studies conducted on COVID CT images, including the COVID-CT dataset. Experimental results indicate that, compared to current advanced channel attention mechanisms, the ECEF module exhibits superior performance in COVID CT image classification tasks. The successful application of the ECEF module establishes a foundation for its widespread use in areas such as COVID CT medical image segmentation and object detection.

Improving object detection in optical devices using a multi-hierarchical cyclable structure-aware rain removal network.

Rain streaks pose a significant challenge to optical devices, impeding their ability to accurately recognize objects in images. To enhance the recognition capabilities of these devices, it is imperative to remove rain streaks from images prior to processing. While deep learning techniques have been adept at removing rain from the high-frequency components of images, they often neglect the low-frequency components, where residual rain streaks can persist. This oversight can severely limit the effectiveness of deraining methods and consequently, the object recognition rate in optical devices such as cameras and smartphones. To address this problem, we developed a novel multi-hierarchical cyclable structure-aware rain removal network (MCS-RRN), which effectively retains the background structure while removing rain streaks, improving the object recognition rate in images. Unlike state-of-the-art approaches that incorporate wavelet transform, our network maintained the low-frequency sub-images and integrated them into a structure-aware subnetwork. We also transferred low-frequency structural information to detail enhancement sub-networks to enhance detailed information and facilitate convergence; this enhanced the capability of our network to eliminate rain streaks in high frequency. In summary, we used a structure information blending module and inverse wavelet transform to fuse derained low-frequency sub-images and achieve rain removal while improving the object recognition rate with the combination of YOLO. Experimental results demonstrated that our method significantly enhances the object recognition rate in images.

Environmentally adaptive fast object detection in UAV images

Detecting objects in aerial images poses a challenging task due to the presence of numerous small objects and complex environmental information. To address these problems, we propose an efficient detector specifically designed for aerial images, named EAF-YOLOv8, based on YOLOv8-S. In this paper, we introduce a novel backbone network called EAFNet, specifically designed for small object detection. EAFNet consists of the Rapidly Merging Receptive Fields Aggregation Module (RMRFAM) and Multi-Scale Channel Attention (MSCA). The RMRFAM utilizes dilated convolution (DConv) and partial convolution (PConv) to acquire richer receptive fields, capturing more extensive contextual information at higher levels while reducing redundancy in channel information, thereby accelerating inference speed. Furthermore, inspired by denoising tasks, we focus on the feature information surrounding the target background and propose MSCA. MSCA integrates channel attention with an embedded self-attention feature pyramid, extending the feature learning scope to the surrounding environment of the target, beyond the target itself. This approach utilizes enhanced background features to elicit a higher response for small targets, reducing false positives. Experimental results demonstrate that in UAVDT and VisDrone2019, the proposed EAF-YOLOv8 achieves mAP50 scores of 34.3% and 49.7%, respectively. Additionally, EAF-YOLOv8 exhibits high real-time inference speeds of 77.60 FPS and 55.56 FPS, showcasing competitive detection performance.

INTEGRATION OF RESUNET AND YOLO ALGORITHMS INTO A UNIFIED MODEL FOR OBJECTS DETECTION

<p>Automatic extraction of footprints of buildings from orthophotos is a challenge in the field of remote sensing data processing. The combination of image classification and object localization tasks in this research aims to develop a new model based on ResUNet and the YOLO algorithm. By applying the proposed model and publicly available data, a high level of building extraction success of 89% is achieved. Although there is potential to improve the results by introducing other types of data, the integration of these models represents a significant step towards improving the technology of automatic extraction of buildings from orthophotos.</p>

Screening COVID-19 from chest X-ray images by an optical diffractive neural network with the optimized F number

The COVID-19 pandemic continues to significantly impact people’s lives worldwide, emphasizing the critical need for effective detection methods. Many existing deep learning-based approaches for COVID-19 detection offer high accuracy but demand substantial computing resources, time, and energy. In this study, we introduce an optical diffractive neural network (ODNN-COVID), which is characterized by low power consumption, efficient parallelization, and fast computing speed for COVID-19 detection. In addition, we explore how the physical parameters of ODNN-COVID affect its diagnostic performance. We identify the F number as a key parameter for evaluating the overall detection capabilities. Through an assessment of the connectivity of the diffractive network, we established an optimized range of F number, offering guidance for constructing optical diffractive neural networks. In the numerical simulations, a three-layer system achieves an impressive overall accuracy of 92.64% and 88.89% in binary- and three-classification diagnostic tasks. For a single-layer system, the simulation accuracy of 84.17% and the experimental accuracy of 80.83% can be obtained with the same configuration for the binary-classification task, and the simulation accuracy is 80.19% and the experimental accuracy is 74.44% for the three-classification task. Both simulations and experiments validate that the proposed optical diffractive neural network serves as a passive optical processor for effective COVID-19 diagnosis, featuring low power consumption, high parallelization, and fast computing capabilities. Furthermore, ODNN-COVID exhibits versatility, making it adaptable to various image analysis and object classification tasks related to medical fields owing to its general architecture.

Deep learning based MA detection with modified ResNet-50

ABSTRACT A deep understanding of retinal images is used to identify vascular diseases, such as Diabetic Retinopathy (DR) in individuals who experience high blood sugar levels and high blood pressure. DR is a progressive disease that starts from minute red saccular out pouches on blood vessels known as Micro-Aneurysm (MA). DR can be cured by eradicating MA on the retina. Detecting microaneurysms (MAs) in retinal digital images is a challenging task due to various factors. These factors include the diverse sizes, shapes, levels of noise, and contrasts exhibited by the images found in the publicly available datasets for Diabetic Retinopathy (DR). Moreover, the limited number of labelled examples in these datasets and the inherent difficulty faced by deep learning algorithms in accurately identifying small objects in retinal digital images further contribute to the complexity involved in MA detection. Here proposing a Deep Learning based MA detection using modified ResNet-50 with a Support Vector Machine. The suggested approach was training, tuning, and evaluation, both qualitatively and quantitatively, using publicly available datasets like E-ophthaMA and DIARETDB1. The suggested approach demonstrates improved outcomes in terms of time efficiency and resource utilisation.

LRMSNet: A New Lightweight Detection Algorithm for Multi-Scale SAR Objects

In recent years, deep learning has found widespread application in SAR image object detection. However, when detecting multi-scale targets against complex backgrounds, these models often struggle to strike a balance between accuracy and speed. Furthermore, there is a continuous need to enhance the performance of current models. Hence, this paper proposes LRMSNet, a new multi-scale target detection model designed specifically for SAR images in complex backgrounds. Firstly, the paper introduces an attention module designed to enhance contextual information aggregation and capture global features, which is integrated into a backbone network with an expanded receptive field for improving SAR image feature extraction. Secondly, this paper develops an information aggregation module to effectively fuse different feature layers of the backbone network. Lastly, to better integrate feature information at various levels, this paper designs a multi-scale aggregation network. We validate the effectiveness of our method on three different SAR object detection datasets (MSAR-1.0, SSDD, and HRSID). Experimental results demonstrate that LRMSNet achieves outstanding performance with a mean average accuracy (mAP) of 95.2%, 98.9%, and 93.3% on the MSAR-1.0, SSDD, and HRSID datasets, respectively, with only 3.46 M parameters and 12.6 G floating-point operation cost (FLOPs). When compared with existing SAR object detection models on the MSAR-1.0 dataset, LRMSNet achieves state-of-the-art (SOTA) performance, showcasing its superiority in addressing SAR detection challenges in large-scale complex environments and across various object scales.

Dual-branch interactive cross-frequency attention network for deep feature learning

As random noises contained in the high-frequency data could interfere with the feature learning of deep networks, low-pass filtering or wavelet transform have been integrated with deep networks to exclude the high-frequency component of input image. However, useful image details like contour and texture are also lost in such a process. In this paper, we propose Dual-branch interactive Cross-frequency attention Network (DiCaN) to separately process low-frequency and high-frequency components of input image, such that useful information is extracted from high-frequency data and included in deep learning. Our DiCaN first decomposes input image into low-frequency and high-frequency components using wavelet decomposition, and then applies two parallel residual-style branches to extract features from the two components. We further design an interactive cross-frequency attention mechanism, to highlight the useful information in the high-frequency data and interactively fuse them with the features in low-frequency branch. The features learned by our framework are then applied for both image classification and object detection and tested using ImageNet-1K and COCO datasets. The results suggest that DiCaN achieves better classification performance than different ResNet variants. Both one-stage and two-stage detectors with our DiCaN backbone also achieve better detection performance than that with ResNet backbone. The code of DiCaN will be released.

PlantSR: Super-Resolution Improves Object Detection in Plant Images.

Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improving model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-of-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, with the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, with the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets related to this study are available at Github.

Machine Learning in Geosciences: A Review of Complex Environmental Monitoring Applications

This is a systematic literature review of the application of machine learning (ML) algorithms in geosciences, with a focus on environmental monitoring applications. ML algorithms, with their ability to analyze vast quantities of data, decipher complex relationships, and predict future events, and they offer promising capabilities to implement technologies based on more precise and reliable data processing. This review considers several vulnerable and particularly at-risk themes as landfills, mining activities, the protection of coastal dunes, illegal discharges into water bodies, and the pollution and degradation of soil and water matrices in large industrial complexes. These case studies about environmental monitoring provide an opportunity to better examine the impact of human activities on the environment, with a specific focus on water and soil matrices. The recent literature underscores the increasing importance of ML in these contexts, highlighting a preference for adapted classic models: random forest (RF) (the most widely used), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), convolutional neural networks (CNNs), principal component analysis (PCA), and much more. In the field of environmental management, the following methodologies offer invaluable insights that can steer strategic planning and decision-making based on more accurate image classification, prediction models, object detection and recognition, map classification, data classification, and environmental variable predictions.

AeroDetectNet: a lightweight, high-precision network for enhanced detection of small objects in aerial remote sensing imagery

Object detection in remote sensing imagery exhibits difficulties due to complex backgrounds, diverse object scales, and intricate spatial context relationships. Motivated by the problems mentioned above, this paper introduces AeroDetectNet, a novel lightweight and high-precision object detection network custom-designed for aerial remote sensing scenarios, building upon the YOLOv7-tiny algorithm. It enhances performance through four key improvements: the normalized Wasserstein distance for consistent object size sensitivity, the Involution module for reduced background noise, a self-designed RCS-biformer module for better spatial context interpretation, and a self-designed WF-CoT SPPCSP feature pyramid for improved feature map weighting and context capture. Ablation studies conducted on a hybrid dataset composed of three open-source remote sensing datasets (including NWPU VHR-10 remote sensing images, RSOD remote sensing images, and VisDrone UAV images) have demonstrated the effectiveness of four improvements specifically for small-size object detection. Visualizations through Grad-CAM further demonstrate AeroDetectNet’s capacity to extract and focus on key object features. Upon individual testing across three open-source datasets, AeroDetectNet has successfully demonstrated its ability to identify objects in images with a smaller pixel area. Through experimental comparisons with other related studies, the AeroDetectNet achieved a competitive mAP while maintaining fewer model parameters, highlighting its highly accurate and lightweight properties.

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on DOTA

In this paper, we proposed comparative research on the classification of various objects in satellite images using some pre-trained models of CNN (VGG-16, Inception-V3, ResNet-50, EfficientNet-B7) and R-CNN. In this research work, we have used the DOTA dataset, which combines data from 14 classes. We have implemented above mentioned pre-trained models of CNN, and R-CNN to achieve optimal results for accuracy as well as productivity. To detect objects like ships, tennis courts, swimming pools, vehicles, and harbors from remotely accessed images. In this study, we have used a convolutional neural network (CNN) as the base model. The transfer learning mechanism is employed to speed up the results and for complex computations. We have discovered with the help of experimental analysis that R-CNN and Inception-V3 are performing best out of the five pre-trained models.

Comparing hybrid models for recognising objects in thermal images at nighttime

This research aims to revolutionize urban object recognition by developing cloud-based Python programs using intelligent algorithms. Unlike current models that focus on colour enhancement in nighttime thermal images, this work addresses the critical challenge of accurate object detection in urban landscapes. The proposed method incorporates a binary generative adversarial network (GAN) generator that can switch bidirectionally between daytime colour (DC) and nighttime infrared (NTIR) images. memory-based visual image memory (MVAM), system extracts important descriptive information from urban landscape images, reducing problems related to small sample sizes. This discussion presents a comprehensive improvement and evaluation of a deep learning image classification pipeline using Google Colab, demonstrating advanced image processing. Using TensorFlow, Keres and scikit image libraries combined with advanced algorithms such as DenseNet121 and MobileNetV2 presents a clear approach. We created a Bidirectional GAN + MVAM for object recognition in this work. Our method performed well, with an accuracy of 81.43%, precision of 51.16, recall of 50.11, and F-score of 46.37. The systematic presentation of the code presents a careful strategy to ensure optimal performance, stability, and efficiency of deep learning and image processing tasks.