Texture Information Research Articles

EDUNet++: An Enhanced Denoising Unet++ for Ice-Covered Transmission Line Images

New technology has made it possible to monitor and analyze the condition of ice-covered transmission lines based on images. However, the collected images are frequently accompanied by noise, which results in inaccurate monitoring. Therefore, this paper proposes an enhanced denoising Unet++ for ice-covered transmission line images (EDUNet++). This algorithm mainly comprises three modules: a feature encoding and decoding module (FEADM), a shared source feature fusion module (SSFFM), and an error correction module (ECM). In the FEADM, a residual attention module (RAM) and a multilevel feature attention module (MFAM) are proposed. The RAM incorporates the cascaded residual structure and hybrid attention mechanism, that effectively preserve the mapping of feature information. The MFAM uses dilated convolution to obtain features at different levels, and then uses feature attention for weighting. This module effectively combines local and global features, which can better capture the details and texture information in the image. In the SSFFM, the source features are fused to preserve low-frequency information like texture and edges in the image, hence enhancing the realism and clarity of the image. The ECM utilizes the discrepancy between the generated image and the original image to effectively capture all the potential information in the image, hence enhancing the realism of the generated image. We employ a novel piecewise joint loss. On the dataset of ice-covered transmission lines, PSNR (peak signal to noise ratio) and SSIM (structural similarity) achieved values of 29.765 dB and 0.968, respectively. Additionally, the visual effects exhibited more distinct detailed features. The proposed method exhibits superior noise suppression capabilities and robustness compared to alternative approaches.

Infrared Small Target Detection Based on Density Peak Search and Local Features

The detection of small infrared targets is still a challenging task and efficient and accurate detection plays a key role in modern infrared search and tracking military applications. However, small infrared targets are difficult to detect due to their weak brightness, small size and lack of shape, structure, texture, and other information elements. In this paper, we propose a target detection method. First, to address the problem that the proximity of targets to high-brightness clutter leads to missed detection of candidate targets, a Gaussian differential filtering preprocessed image is used to suppress high-brightness clutter. Second, a density-peaked global search method is used to determine the location of candidate targets in the preprocessed image. We then use local contrast to the candidate target points to enhance the gradient features and suppress background clutter. The Facet model is used to compute multidirectional gradient features at each point. A new efficient surrounding symmetric region partitioning scheme is constructed to capture the gradient characteristics of targets of different sizes in eight directions, followed by weighting the candidate target gradient characteristics using the standard deviation of the symmetric region difference. Finally, an adaptive threshold segmentation method is used to extract small targets. Experimental results show that the method proposed in this paper has better detection accuracy and robustness compared with other detection methods.

Critical Information Mining Network: Identifying Crop Diseases in Noisy Environments

When agricultural experts explore the use of artificial intelligence technology to identify and detect crop diseases, they mainly focus on the research of a stable environment, but ignore the problem of noise in the process of image acquisition in real situations. To solve this problem, we propose an innovative solution called the Critical Information Mining Network (CIMNet). Compared with traditional models, CIMNet has higher recognition accuracy and wider application scenarios. The network has a good effect on crop disease recognition under noisy environments, and can effectively deal with the interference of noise to the recognition effect in actual farmland scenes. Consider that the shape of the leaves can be symmetrical or asymmetrical.First, we introduce the Non-Local Attention Module (Non-Local), which uses a unique self-attention mechanism to fully capture the context information of the image. The module overcomes the limitation of traditional convolutional neural networks that only rely on local features and ignore global features. Global features are particularly important when the image is disturbed by noise. Non-Local improves a more comprehensive visual understanding of crop disease recognition. Secondly, we have innovatively designed a Multi-scale Critical Information Fusion Module (MSCM). The module uses the Key Information Extraction Module (KIB) to dig into the shallow key features in the network deeply. The shallow key features strengthen the feature perception of the model to the noise image through texture and contour information, and then the shallow key features and deep features are fused to enrich the original deep feature information of the network. Finally, we conducted experiments on two public datasets, and the results showed that the accuracy of our model in crop disease identification under a noisy environment was significantly improved. At the same time, our model also showed excellent performance under stable conditions. The results of this study provide favorable support for the improvement of crop production efficiency.

Activated Sparsely Sub-Pixel Transformer for Remote Sensing Image Super-Resolution

Transformers have recently achieved significant breakthroughs in various visual tasks. However, these methods often overlook the optimization of interactions between convolution and transformer blocks. Although the basic attention module strengthens the feature selection ability, it is still weak in generating superior quality output. In order to address this challenge, we propose the integration of sub-pixel space and the application of sparse coding theory in the calculation of self-attention. This approach aims to enhance the network’s generation capability, leading to the development of a sparse-activated sub-pixel transformer network (SSTNet). The experimental results show that compared with several state-of-the-art methods, our proposed network can obtain better generation results, improving the sharpness of object edges and the richness of detail texture information in super-resolution generated images.

ER‐Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection

AbstractThe colour‐enhanced point cloud map is increasingly being employed in fields such as robotics, 3D reconstruction and virtual reality. The authors propose ER‐Mapping (Extrinsic Robust coloured Mapping system using residual evaluation and selection). ER‐Mapping consists of two components: the simultaneous localisation and mapping (SLAM) subsystem and the colouring subsystem. The SLAM subsystem reconstructs the geometric structure, where it employs a dynamic threshold‐based residual selection in LiDAR‐inertial odometry to improve mapping accuracy. On the other hand, the colouring subsystem focuses on recovering texture information from input images and innovatively utilises 3D–2D feature selection and optimisation methods, eliminating the need for strict hardware time synchronisation and highly accurate extrinsic parameters. Experiments were conducted in both indoor and outdoor environments. The results demonstrate that our system can enhance accuracy, reduce computational costs and achieve extrinsic robustness.

Diagnostic significance of multisequence MRI radiomics models in distinguishing benign and malignant spinal fractures

PurposeTo investigate the diagnostic value of multi-sequence magnetic resonance imaging (MRI) in benign and malignant spinal fractures. Materials and methodsMRI data of patients with pathologically confirmed malignant vertebral fractures were retrospectively collected and compared with those with benign vertebral fractures. The image omics features of T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) were extracted respectively, and were randomly divided into training and test sets at 8:2. The training set adopted recursive feature elimination method and minimum absolute contraction and selection operator (LASSO) regression to screen the variables. Three image omics models, T1WI, T2WI and the combination of two sequences, were constructed by logistic regression, and the diagnostic efficiency of each model was verified by the test set. The receiver operating curve evaluated the diagnostic efficiency of the model. ResultsA total of 111 vertebrae with fracture were included in 97 patients, 55 with benign fracture and 56 with malignant fracture. In the training set, the AUC, sensitivity and specificity of the T1WI model were 0.892, 88.9% and 81.4%, respectively. In T2WI model, the results were 0.924, 82.2% and 95.3%, respectively. The combined models of the two sequences were 0.934, 90.0% and 87.5%, respectively. In the test set, the AUC, sensitivity and specificity of T1WI model were 0.877, 80.0% and 92.3%, respectively. In T2WI model, the results were 0.923, 90.0% and 92.3%, respectively. The combined models were 0.933, 100.0% and 87.5%, respectively. The diagnostic efficiency of the two-sequence combined image omics model was better than that of the radiologist. ConclusionThe combined model, incorporating T1WI and T2WI, outperformed single-sequence models in terms of diagnostic accuracy. The integration of multisequence radiomic features provided enhanced texture information, contributing to superior accuracy and efficiency in diagnosing malignant spinal fractures.

A Study on the Effect Mechanism of Pectin Modification on the Carrot Cell Wall’s Texture Formation under Ultrasonic and Infrared Drying

The carrot has a high water content, and dehydration is an important means to extend its edible period and reduce storage and transportation costs. In the case of infrared (IR) drying, the porosity of the product is low and the structure is compact; the textural properties of the product are improved by using combined ultrasound and infrared (US-IR) drying; however, there is a lack of reports on the mechanism of this. Pectin has an important influence on the formation of the textural properties of fruit and vegetable tissues. In order to investigate the mechanism of the change in endogenous pectin properties in the carrot cell wall under US-IR drying on the improvement of the textural properties of the product, different fractions of pectins (water-soluble pectin, chelating pectin, alkali-soluble pectin) of the carrot were extracted, separated, and analysed. The thermal stability, component and content changes, Fourier infrared (FTIR), X-ray diffraction (XRD), esterification degree, molecular weight, monosaccharide composition, Ca ion content, and atomic force microscopy (AFM) of the pectins were determined. The results showed that the changes in the contents and properties of the carrot pectins under US-IR conditions had a positive effect on the improvement of the textural properties of the carrot tissues.

Reinforced Res-Unet transformer for underwater image enhancement

Light propagation through water is subject to varying degrees of energy loss, causing captured images to display characteristics of color distortion, reduced contrast, and indistinct details and textures. The data-driven approach offers significant advantages over traditional algorithms, such as improved accuracy and reduced computational costs. However, challenges such as optimizing network architecture, refining coding techniques, and expanding database resources must be addressed to ensure the generation of high-quality reconstructed images across diverse tasks. In this paper, an underwater image enhancement network based on feature fusion is proposed named RUTUIE, which integrates feature fusion techniques. It leverages the strengths of both Resnet and U-shape architecture, primarily structured around a streamlined up-and-down sampling mechanism. Specifically, the U-shaped structure serves as the backbone of ResNet, equipped with two feature transformers at both the encoding and decoding ends, which are linked by a single-stage up-and-down sampling structure. This architecture is designed to minimize the omission of minor features during feature scale transformations. Furthermore, the improved Transformer encoder leverages a feature-level attention mechanism and the advantages of CNNs, endowing the network with both local and global perceptual capabilities. Then, we propose and demonstrate that embedding an adaptive feature selection module at appropriate locations can retain more learned feature representations. Moreover, the application of a previously proposed color transfer method for synthesizing underwater images and augmenting network training. Extensive experiments demonstrate that our work effectively corrects color casts, reconstructs the rich texture information in natural scenes, and improves the contrast.

Detection of Road Crack Images Based on Multistage Feature Fusion and a Texture Awareness Method.

Structural health monitoring for roads is an important task that supports inspection of transportation infrastructure. This paper explores deep learning techniques for crack detection in road images and proposes an automatic pixel-level semantic road crack image segmentation method based on a Swin transformer. This method employs Swin-T as the backbone network to extract feature information from crack images at various levels and utilizes the texture unit to extract the texture and edge characteristic information of cracks. The refinement attention module (RAM) and panoramic feature module (PFM) then merge these diverse features, ultimately refining the segmentation results. This method is called FetNet. We collect four public real-world datasets and conduct extensive experiments, comparing FetNet with various deep-learning methods. FetNet achieves the highest precision of 90.4%, a recall of 85.3%, an F1 score of 87.9%, and a mean intersection over union of 78.6% on the Crack500 dataset. The experimental results show that the FetNet approach surpasses other advanced models in terms of crack segmentation accuracy and exhibits excellent generalizability for use in complex scenes.

The microstructure evolution and strengthening mechanism of fine-grained Al–Mn alloy with nanoscale precipitates during cryorolling

In this work, the fine-grained Al–Mn alloys with nanoscale precipitates was designed, and the texture evolution and strengthening mechanism for fine-grained Al–Mn alloy with nanoscale precipitates during cryorolling were investigated by EBSD and TEM analysis. The results show that the dislocation tangles gradually evolved into dislocation cells, and then into subgrains/ultrafine grains with boosting rolling reduction. The initial grains and precipitates can be significantly refined by cryorolling. Furthermore, the dominating P and M textures in the solution-treated Al–Mn alloy gradually converted to α-fiber and β-fiber textures during cryorolling. The formation of α-fiber and β-fiber textures is beneficial for improving the strength of the Al–Mn alloy. An excellent strength-ductility synchronization can be achieved in the Al–Mn alloy cryorolled at a rolling reduction of 50 %. The strengthening mechanism of Al–Mn alloy subjected to cryorolling is associated with texture strengthening, dislocation strengthening, grain refinement strengthening and precipitation strengthening.

Self-Assembly of Ionic Superdiscs in Nanopores.

Discotic ionic liquid crystals (DILCs) consist of self-assembled superdiscs of cations and anions that spontaneously stack in linear columns with high one-dimensional ionic and electronic charge mobility, making them prominent model systems for functional soft matter. Compared to classical nonionic discotic liquid crystals, many liquid crystalline structures with a combination of electronic and ionic conductivity have been reported, which are of interest for separation membranes, artificial ion/proton conducting membranes, and optoelectronics. Unfortunately, a homogeneous alignment of the DILCs on the macroscale is often not achievable, which significantly limits the applicability of DILCs. Infiltration into nanoporous solid scaffolds can, in principle, overcome this drawback. However, due to the experimental challenges to scrutinize liquid crystalline order in extreme spatial confinement, little is known about the structures of DILCs in nanopores. Here, we present temperature-dependent high-resolution optical birefringence measurement and 3D reciprocal space mapping based on synchrotron X-ray scattering to investigate the thermotropic phase behavior of dopamine-based ionic liquid crystals confined in cylindrical channels of 180 nm diameter in macroscopic anodic aluminum oxide membranes. As a function of the membranes' hydrophilicity and thus the molecular anchoring to the pore walls (edge-on or face-on) and the variation of the hydrophilic-hydrophobic balance between the aromatic cores and the alkyl side chain motifs of the superdiscs by tailored chemical synthesis, we find a particularly rich phase behavior, which is not present in the bulk state. It is governed by a complex interplay of liquid crystalline elastic energies (bending and splay deformations), polar interactions, and pure geometric confinement and includes textural transitions between radial and axial alignment of the columns with respect to the long nanochannel axis. Furthermore, confinement-induced continuous order formation is observed in contrast to discontinuous first-order phase transitions, which can be quantitatively described by Landau-de Gennes free energy models for liquid crystalline order transitions in confinement. Our observations suggest that the infiltration of DILCs into nanoporous solids allows tailoring their nanoscale texture and ion channel formation and thus their electrical and optical functionalities over an even wider range than in the bulk state in a homogeneous manner on the centimeter scale as controlled by the monolithic nanoporous scaffolds.

The effect of heat treatment on the anisotropic mechanical properties of metastable β titanium alloy

The effect of heat treatment on the anisotropic mechanical properties of metastable β titanium alloy is investigated in this work. The anisotropic deformation behaviour is mainly due to the formation of band-like macrozone and texture during forging, which can be effectively weakened by solution + aging treatment. Both the microstructure and texture of the heat treated samples are favourable for the lower strength, higher ductility and impact toughness of axis direction (AD) samples than circumferential direction (CD) ones. Besides, the CD impact samples have stronger anisotropy than AD samples. The delamination along different directions happens in the impact fracture surface of CD samples.

Formation of texture and twinning at 296 K of “Artificial” polycrystals of an equiatomic Co20Cr20Fe20Ni20Mn20 High-entropy alloy

In the present paper, “artificial” polycrystals of the equiatomic Co20Cr20Fe20Ni20Mn20 high-entropy alloy were obtained for the first time via rolling process of single crystals up to 67 % along the 1¯11 and [001] directions in the rolling plane (011) at 296 K and subsequent annealing at 1423 K for 1 h. It was shown that texture depends on the rolling direction. When rolling along the 1¯11 direction, a 〈012〉{112} texture was formed, and along the [001] direction it was the 〈123〉{111} texture. At 296 K, twinning developed after slip deformation of 30 % in “artificial” polycrystals with the 〈123〉{111} texture and was not observed in samples with the 〈012〉{112} texture.

Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery

Leaf nitrogen concentration (LNC) is a primary indicator of crop nitrogen status, closely related to the growth and development dynamics of crops. Accurate and efficient monitoring of LNC is significant for precision field crop management and enhancing crop productivity. However, the biochemical properties and canopy structure of wheat change across different growth stages, leading to variations in spectral responses that significantly impact the estimation of wheat LNC. This study aims to investigate the construction of feature combination indices (FCIs) sensitive to LNC across multiple wheat growth stages, using remote sensing data to develop an LNC estimation model that is suitable for multiple growth stages. The research employs UAV multispectral remote sensing technology to acquire canopy imagery of wheat during the early (Jointing stage and Booting stage) and late (Early filling and Late filling stages) in 2021 and 2022, extracting spectral band reflectance and texture metrics. Initially, twelve sensitive spectral feature combination indices (SFCIs) were constructed using spectral band information. Subsequently, sensitive texture feature combination indices (TFCIs) were created using texture metrics as an alternative to spectral bands. Machine learning algorithms, including partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and Gaussian process regression (GPR), were used to integrate spectral and texture information, enhancing the estimation performance of wheat LNC across growth stages. Results show that the combination of Red, Red edge, and Near-infrared bands, along with texture metrics such as Mean, Correlation, Contrast, and Dissimilarity, has significant potential for LNC estimation. The constructed SFCIs and TFCIs both enhanced the responsiveness to LNC across multiple growth stages. Additionally, a sensitive index, the Modified Vegetation Index (MVI), demonstrated significant improvement over NDVI, correcting the over-saturation concerns of NDVI in time-series analysis and displaying outstanding potential for LNC estimation. Spectral information outperforms texture information in estimation capability, and their integration, particularly with SVR, achieves the highest precision (coefficient of determination (R2) = 0.786, root mean square error (RMSE) = 0.589%, and relative prediction deviation (RPD) = 2.162). In conclusion, the sensitive FCIs developed in this study improve LNC estimation performance across multiple growth stages, enabling precise monitoring of wheat LNC. This research provides insights and technical support for the construction of sensitive indices and the precise management of nitrogen nutrition status in field crops.

Image inpainting based on GAN-driven structure- and texture-aware learning with application to object removal

In this paper, a novel deep learning-based image inpainting framework consisting of restoring image structure and reconstructing image details from corrupted images is proposed. Most image inpainting methods in the literature aim at restoring image details, outlines, and colors, simultaneously, which may suffer from blurring, deformation, and unreasonable content recovery due to interference among various information. To solve these problems, a two-stage image inpainting deep neural network based on GAN (generative adversarial network) architecture is proposed. The proposed inpainting framework consists of two modules: (1) the first stage, called the structure-aware learning stage, aims at learning a GAN-based structure restoration network, focusing on recovering the low-frequency image component, including colors and outlines of the missing regions of the input corrupted image; and (2) the second stage, called the texture-aware learning stage, aims at learning a GAN-based detail refinement network, focusing on rebuilding the high-frequency image details and texture information. In particular, we also propose to remove details from the training images to better train the structure restoration network to avoid inadequate image structure recovery induced by richer image textures, where the detail reconstruction task is left to the second stage. This strategy achieves to balance the workload between the two stages and the image quality can be progressively enhanced through the two stages. Experimental results have shown that the proposed deep inpainting framework quantitatively and qualitatively achieves state-of-the-art performance on the well-known datasets, including the CelebA, Places2, and ImageNet datasets, compared with existing deep learning-based image inpainting approaches. More specifically, in terms of the two well-known image quality assessment metrics, PSNR (peak signal-to-noise ratio) and SSIM (structural similarity), the improvement percentage of the proposed method, compared with the baseline approach, respectively, ranges from 3.23 % to 11.12 %, and 1.95 % to 13.39 %. The improvements have been shown to stably and significantly outperform the compared state-of-the-art methods in most types of inpainting mask. We also show that the proposed method is applicable to image editing in object removal from a single image.

Denoising of Wrapped Phase in Digital Speckle Shearography Based on Convolutional Neural Network

Speckle-shearing technology is widely used in defect detection due to its high precision and non-contact characteristics. However, the wrapped-phase recording defect information is often accompanied by a lot of speckle noise, which affects the evaluation of defect information. To solve the problems of traditional denoising algorithms in suppressing speckle noise and preserving the texture features of wrapped phases, this study proposes a speckle denoising algorithm called a speckle denoising convolutional neural network (SDCNN). The proposed method reduces the loss of texture information and the blurring of details in the denoising process by optimizing the loss function. Different from the previous simple assumption that the speckle noise is multiplicative, this study proposes a more realistic wrapped image-simulation method, which has better training results. Compared with representative algorithms such as BM3D, SDCNN can handle a wider range of speckle noise and has a better denoising effect. Simulated and real speckle-noise images are used to evaluate the denoising effect of SDCNN. The results show that SDCNN can effectively reduce the speckle noise of the speckle-shear wrapping phase and retain better texture details.

Twist2 contributes to skin regeneration and hair follicle formation in mouse fetuses

Unlike adult mammalian wounds, early embryonic mouse skin wounds completely regenerate and heal without scars. Analysis of the underlying molecular mechanism will provide insights into scarless wound healing. Twist2 is an important regulator of hair follicle formation and biological patterning; however, it is unclear whether it plays a role in skin or skin appendage regeneration. Here, we aimed to elucidate Twist2 expression and its role in fetal wound healing. ICR mouse fetuses were surgically wounded on embryonic day 13 (E13), E15, and E17, and Twist2 expression in tissue samples from these fetuses was evaluated via in situ hybridization, immunohistochemistry, and reverse transcription-quantitative polymerase chain reaction. Twist2 expression was upregulated in the dermis of E13 wound margins but downregulated in E15 and E17 wounds. Twist2 knockdown on E13 left visible marks at the wound site, inhibited regeneration, and resulted in defective follicle formation. Twist2-knockdown dermal fibroblasts lacked the ability to undifferentiate. Furthermore, Twist2 hetero knockout mice (Twist + /-) formed visible scars, even on E13, when all skin structures should regenerate. Thus, Twist2 expression correlated with skin texture formation and hair follicle defects in late mouse embryos. These findings may help develop a therapeutic strategy to reduce scarring and promote hair follicle regeneration.

Anomalous ductility-strength synergy occurred after cold rolling in a high Gd content Mg alloy

During cold rolling in magnesium (Mg) alloys, there will always be serious ductility deterioration, which greatly restricts the application of this processing method. In this work, ductility-strength synergy occurred after cold rolling was carried out on a 〈0001〉 fiber textured Mg-17.5Gd-Zr alloy (wt.%). The ductility-strength synergy can be mainly attributed to texture randomization, microstructure refinement, and easier basal slip transfer condition in the twinned grains. Firstly, the activation of multiple twinning behaviors, i.e., {10−12}, {10−11}, and {11−21} twinning, and related twin-twin interaction restrict the formation of strong basal texture and facilitate the texture randomization. Secondly, profuse high-angle grain boundaries form through dislocation-twin interaction, twin-twin interaction, and dislocation interaction, which promote microstructure refinement. Thirdly, the basal slip transfer becomes easier in the twinned grains owing to the activation of multiple twinning behaviors and their impingements. Activation of {11−21} twinning and 〈c + a〉 dislocations and the 〈0001〉 fiber texture which is favorable for multiple twinning activating during cold rolling are considered to be the key roles for microstructure and texture optimization.

FS_YOLOv8: A Deep Learning Network for Ground Fissures Instance Segmentation in UAV Images of the Coal Mining Area

Abstract. The ground fissures caused by coal mining have seriously affected the ecological environment of the land. Timely and accurate identification and landfill treatment of ground fissures can avoid secondary geological disasters in coal mine areas. At present, the fissure identification methods based on deep learning show excellent performance on roads and walls, etc. Nevertheless, the automatic and reliable segmentation of ground fissures in remote sensing images poses a challenge for deep learning networks, due to the diverse and complex texture information included in the mining ground fissures and background. To overcome these challenges, we propose an improved YOLOv8 instance segmentation network to automatically and efficiently segment the ground fissures in coal mining areas. In detail, a model called FS_YOLOv8 is proposed. The DSPP (Dynamic Snake convolutional Pyramid Pooling) module is incorporated into the FS_YOLOv8 model to establish a multi-scale dynamic snake convolution feature aggregation structure. This module replaces the conventional convolution found in the SPPF module of YOLOv8 and aims to enhance the model's ability to extract features related to fissures with tubular structures. Furthermore, the D-LKA (Deformable Large Kernel Attention) module is employed to autonomously collect fissure context information. To enhance the detection capability of challenging samples in remote sensing images with intricate background and fissure texture, we employ a Slide Loss function. Ultimately, the ground fissure dataset of unmanned aerial vehicle (UAV) images in coal mine areas is subjected to experimental analysis. The experimental findings demonstrate that FS_YOLOv8 exhibits exceptional proficiency in segmenting ground fissures within intricate and expansive mining areas.

Effective microorganism solution-imbued sustainable self-curing concrete: Evaluation of sorptivity, drying shrinkage and expansion

Production of various concrete elements at early ages requires careful water curing to ensure the development of a hydration reaction that fills and discontinues the capillary voids, thus achieving the desired strength of the hydrated products. In real practice, problems occur where appropriate curing is tricky to attain targeted structural components like inclined, high-rise, and thick concrete elements. To meet this goal, this study evaluated the engineering properties of some newly designed concrete incorporated with effective microorganism (EM) solutions as new self-curing agents at varying contents (5, 10, 15, 20, and 25% in place of water). The casted specimens were subjected to wet and dry curing regimes. The effects of EM solutions at various contents on the strength development, sorptivity, drying shrinkage, expansion, and microstructures of the proposed concrete were determined. The results indicated that the specimens containing 10% of EM (optimum) as water replacement achieved the highest strength (48.9 MPa) at 28 days of curing. Compared to the control sample, the permeability, drying shrinkage, and expansion of the specimen made with 10% of EM were correspondingly reduced by 2.5%, 48%, and 49%. In addition, the optimum sample displayed denser texture and gel formation with fewer voids than other specimens. The optimum specimen exhibited nearly 12% more hydration than normal concrete in water curing. It was established that an EM solution in place of water in concrete can serve as an effective self-curing agent for the sustainable growth of high-performance construction materials.