Local Segmentation Research Articles

Initial Conditions for the Formation of Insurance Protection as the Basis for the Construction of the Concept of Insurance of Private Households

High-quality insurance protection of households contributes to ensuring their financial security in the face of possible negative changes leading to budget imbalance. At the same time, the formation of insurance plans should take into account the specifics of building insurance relations, determined by the type of household and other key parameters that affect the quality of insurance coverage provided. The scientific novelty of the research lies in solving a local scientific problem associated with the consistent development of the theoretical foundations for the formation of insurance protection for private households and obtaining results that are of practical importance for the full implementation of insurance functions in relation to a specific institutional group. The purpose of the study was to identify and summarize key positions that can have a potential impact on the content, volume and completeness of insurance coverage provided to private households. At this stage, the researcher had two tasks: to identify operational differences between insurance protection of monosubject and polysubject households; to generalize factors that can influence the volume of insurance protection being formed and its orientation. The author came to the conclusion that among them can be attributed the functional orientation of insurance protection; the level of formation of the insurance plan; the behavioral attitude of potential policyholders and the motives of their activity in local segments of the insurance market.

Effects of cross‐linked networks on dielectric properties of epoxy resins based on molecular dynamics

AbstractIn this paper, three epoxy resin systems commonly used in power equipment are prepared to obtain cross‐linked networks with structural differences. The relationship between microscopic structures and dielectric properties of epoxy resins is investigated and discussed. Experimental results show that the polarization and conductance losses are inhibited in the anhydride‐cured systems with methyl groups. The molecular dynamics (MD) simulation shows that rigid anhydride structures (such as methyl groups) play a major role in reducing the local segment mobility and increasing the free volume at cross‐linking points. The decrease in local segment mobility has been confirmed by the decrease of mean square displacement (MSD) at the cross‐linking points, which is consistent with the change of measured polarization and conductance loss. In the glassy state, the dielectric properties of different anhydride‐cured systems can be reflected by local MSD at the cross‐linking points. At high temperatures near the glass transition temperature, both the free volume and network mobility increase significantly, which reflects the increased dielectric relaxation strength and conductance loss. The understanding of the structure–property relationships could provide a theoretical foundation for epoxy modification in a controlled manner for power equipment applications.

Effect of local openings on bearing behavior and failure mechanism of shield tunnel segments

Local failures (loss of concrete or reinforcement) can severely compromise the bearing capacity of shield segments, damaging the tunnel structures. To investigate the effects of local openings on the bearing behavior and failure mechanism, four full-scale bending tests were conducted on specimens with different opening positions and diameters; monitoring of load, displacement, and concrete strain was performed during loading. The test results reveal that both the opening position and diameter significantly influence the bearing characteristics of the segment. The failure process includes four sequential stages distinguished by three critical loads, namely the cracking, failure, and ultimate loads. Subsequently, the numerical model of the local failure segment was established using the elastoplastic damage constitutive relation of the concrete and verified by inversing the full-scale test results. Based on the numerical model, parametric analyses were performed to comprehensively investigate the influences of the opening position, concrete loss, and reinforcement loss on the bending capacity. Furthermore, an analytical model was proposed, indicating that the opening position is the primary factor decreasing the bearing capacity, followed by the opening diameter and reinforcement loss. The results of this study can provide a theoretical basis for the safety assessment and remedial design of subway shield tunnels under extreme breakthrough conditions.

Heat transfer performance enhancement and mechanism analysis of thermal energy storage unit designed by using a modified transient topology optimization model

The spatial layout of the highly conductive fin and phase change materials (PCM) and the thermophysical properties of PCM are important factors restricting the heat transfer rate of the latent heat thermal energy storage (LHTES). The current fin design concentrates on the limited design space, and rarely adopts the optimizing form of unconstrained structure evolution in the process. So this study proposes a modified transient topology optimization (TO) model to seek the optimal distribution of the fins embedded into PCM subject to the constraints of fin area and specified time. This topology optimization problem is formulated using the density-based material design variable in governing equations, physical constraints, etc. The design freedom and procedural iterative solving of TO model allow for the free evolution of innovative fin structures and elucidate the interaction mechanism between the fin design and physical occurring processes. The double interpolation function, variable regularization, adjoint-based sensitivity analysis and GCMMA algorithm are established to further improve TO model to obtain high-precision solutions. Using above methods, the regular evolution mechanism of fins and physical performance parameters and the trade-off between performance and structure are analyzed. Then, the effects and reasons of pure paraffin (PW) and the adding carbon nanotubes (nPW) on changes in topology structure and performance parameters are also comparatively studied. Finally, by comparing TO-fins with conventional rectangular fins, the superiority of the established method is verified, and the performance improvement mechanism and extent of TO-fins are revealed. Results show that TO-fin LHTES with nPW can achieve better performance than TO-fin LHTES with PW under fewer branches and shorter branch length. The uniform distribution and multi-branch characteristics of TO-fins greatly shorten the heat conduction path, and its local segmentation of whole PCM enhances the local convection effect.

Accurate and robust auto-segmentation of head and neck organ-at-risks based on a novel CNN fine-tuning workflow.

Obvious inconsistencies in auto-segmentations exist among various AI software. In this study, we have developed a novel convolutional neural network (CNN) fine-tuning workflow to achieve precise and robust localized segmentation. The datasets include Hubei Cancer Hospital dataset, Cetuximab Head and Neck Public Dataset, and Québec Public Dataset. Seven organs-at-risks (OARs), including brain stem, left parotid gland, esophagus, left optic nerve, optic chiasm, mandible, and pharyngeal constrictor, were selected. The auto-segmentation results from four commercial AI software were first compared with the manual delineations. Then a new multi-scale lightweight residual CNN model with an attention module (named as HN-Net) was trained and tested on 40 samples and 10 samples from Hubei Cancer Hospital, respectively. To enhance the network's accuracy and generalization ability, the fine-tuning workflow utilized an uncertainty estimation method for automatic selection of candidate samples of worthiness from Cetuximab Head and Neck Public Dataset for further training. The segmentation performances were evaluated on the Hubei Cancer Hospital dataset and/or the entire Québec Public Dataset. A maximum difference of 0.13 and 0.7mm in average Dice value and Hausdorff distance value for the seven OARs were observed by four AI software. The proposed HN-Net achieved an average Dice value of 0.14 higher than that of the AI software, and it also outperformed other popular CNN models (HN-Net: 0.79, U-Net: 0.78, U-Net++: 0.78, U-Net-Multi-scale: 0.77, AI software: 0.65). Additionally, the HN-Net fine-tuning workflow by using the local datasets and external public datasets further improved the automatic segmentation with the average Dice value by 0.02. The delineations of commercial AI software need to be carefully reviewed, and localized further training is necessary for clinical practice. The proposed fine-tuning workflow could be feasibly adopted to implement an accurate and robust auto-segmentation model by using local datasets and external public datasets.

Anatomical Variations in Fissure of the Lung on Computed Tomography

Objective: Lung fissures embryologically separate the bronchopulmonary segments from each other. We aimed to detect anatomical variations in fissures in patients who underwent thoracic computed tomography (CT). Materials and Methods: All the patients underwent a thoracic CT examination between July 1 - July 15, 2022. The patients’ gender, lung fissures continuity, accessory fissures presence, and variation side were recorded. The frequency of fissures was compared between the genders using the chi-square test. Results: The study included a total of 352 patients (211 men, 141 women). A total of 105 variations were detected in 95/352 (26.99%) of the patients, 61/211 (28.91%) were male, 34/141 (24.11%) were female. The right oblique fissure was incomplete in nine (2.6%), and the right horizontal fissure was incomplete in 14 (4%) patients and absent in 14 (4%). The left oblique fissure was observed to be incomplete in 16 (4.5%) patients. A total of 52 (14.8%) accessory fissures were detected. Conclusion: In the literature, a wide variety of fissure variations have been reported. Due to this diversity, having good knowledge of the fissure anatomical architecture is essential when performing surgical procedures and interpreting radiological images to clinically identify the location of bronchopulmonary segments.

An improved DeepLabv3+ lightweight network for remote-sensing image semantic segmentation

To improve the accuracy of remote-sensing image semantic segmentation in complex scenario, an improved DeepLabv3+ lightweight neural network is proposed. Specifically, the lightweight network MobileNetv2 is used as the backbone network. In atrous spatial pyramid pooling (ASPP), to alleviate the gridding effect, the Dilated Convolution in original DeepLabv3+ network is replaced with the Hybrid Dilated Convolution (HDC) module. In addition, the traditional spatial mean pooling is replaced by the strip pooling module (SPN) to improve the local segmentation effect. In the decoder, to obtain the rich low-level target edge information, the ResNet50 residual network is added after the low-level feature fusion. To enhance the shallow semantic information, the efficient and lightweight Normalization-based Attention Module (NAM) is added to capture the feature information of small target objects. The results show that, under the INRIA Aerial Image Dataset and same parameter setting, the Mean Pixel Accuracy (MPA) and Mean Intersection over Union (MIoU) are generally best than DeepLabv3+ , U-Net, and PSP-Net, which are respectively improved by 1.22%, − 0.22%, and 2.22% and 2.17%, 1.35%, and 3.42%. Our proposed method has also a good performance on the small object segmentation and multi-object segmentation. What’s more, it significantly converges faster with fewer model parameters and stronger computing power while ensuring the segmentation effect. It is proved to be robust and can provide a methodological reference for high-precision remote-sensing image semantic segmentation.

The spatial dynamics of local violence, 2017–2021: examining the distribution and predictors of changes in street segment violence during the “crime surge”

ABSTRACT Aggravated assault and homicide both increased in 2020 and 2021 in the United States, generating considerable public concern. As such, the current study examines whether (1) the increase in violence during the 2020–2021 period uniquely affected some local street segments more so than others when compared to the previous years 2017–2019 and, in turn, (2) whether specific socio-demographic characteristics are associated with different changes in local violence during the 2020–2021 period. The current study focuses on the City of Little Rock, Arkansas, examining violence across streets nested within block groups. Descriptive analyses are followed by mixed-effect models to examine the potential change (increase, decrease) or stability in violence occurrence, including whether a first incident occurred in the years 2020–2021. The central finding was that the violence surge only impacted a small number of street segments – just over 14% of the total sample in Little Rock – and roughly the same proportion experienced a decrease in violence during this same period of time. The results are further discussed from a crime prevention and policing perspective.

JSMNET: IMPROVING INDOOR POINT CLOUD SEMANTIC AND INSTANCE SEGMENTATION THROUGH SELF-ATTENTION AND MULTISCALE FUSION

Abstract. The semantic understanding of indoor 3D point cloud data is crucial for a range of subsequent applications, including indoor service robots, navigation systems, and digital twin engineering. Global features are crucial for achieving high-quality semantic and instance segmentation of indoor point clouds, as they provide essential long-range context information. To this end, we propose JSMNet, which combines a multi-layer network with a global feature self-attention module to jointly segment three-dimensional point cloud semantics and instances. To better express the characteristics of indoor targets, we have designed a multi-resolution feature adaptive fusion module that takes into account the differences in point cloud density caused by varying scanner distances from the target. Additionally, we propose a framework for joint semantic and instance segmentation by integrating semantic and instance features to achieve superior results. We conduct experiments on S3DIS, which is a large three-dimensional indoor point cloud dataset. Our proposed method is compared against other methods, and the results show that it outperforms existing methods in semantic and instance segmentation and provides better results in target local area segmentation. Specifically, our proposed method outperforms PointNet (Qi et al., 2017a) by 16.0% and 26.3% in terms of semantic segmentation mIoU in S3DIS (Area 5) and instance segmentation mPre, respectively. Additionally, it surpasses ASIS (Wang et al., 2019) by 6.0% and 4.6%, respectively, as well as JSPNet (Chen et al., 2022) by a margin of 3.3% for semantic segmentation mIoU and a slight improvement of 0.3% for instance segmentation mPre.

Evaluation of the Transport Environmental Effects of an Urban Road Network in a Medium-Sized City in a Developing Country

A decision support model (DSM) involving a combination of five different prediction models for the environmental effects of transport and the powerful HMADM approach was introduced for the first time to assess the multiple criteria environmental effects of transport in an urban road network of the Khon Kaen Metropolitan Municipality (KKMM) in Khon Kaen City, Thailand. Five mathematical models were adopted to quantify the CO2 emissions (CO2Es), PM2.5 concentration (PM2.5C), CO concentrations (COCs), noise levels (NOLs), and pedestrian accident risk (PAR) values of all road segments in the study area. The FAHP, FSM, and TOPSIS were integrated into the HMADM to estimate the composite transport environmental effect scores (CTEESs) of each road segment. The FAHP was applied to determine the relative weights of each environmental criterion for three land use types, and the FSM was utilized to transform linguistic (fuzzy) scores into numerical (crisp) scores. Both the FAHP and FSM are principally used to deal with uncertain, incomplete, and ambiguous (fuzzy) information that appears during decision-making processes. Finally, TOPSIS was used to estimate the CTEESs of each road segment. An integrated DSM was applied to comprehend and evaluate each individual environmental criterion and the combined environmental criteria for each road segment in the study area. The DSM was employed to rank the problematic locations of all road segments. For instance, the ranking of the top 12 road segments with the greatest CTEESs was 75, 80, 48, 89, 76, 5, 64, 59, 60, 16, 65, and 62. In addition, this DSM can also be used to identify the possible causes of such locations and allocate limited government budgets for the implementation of appropriate remedial measures for resolving such environmental problems due to transport in an urban road network in the study area.

Upper extremity kinematics: development of a quantitative measure of impairment severity and dissimilarity after stroke.

Strokes are a leading cause of disability worldwide, with many survivors experiencing difficulty in recovering upper extremity movement, particularly hand function and grasping ability. There is currently no objective measure of movement quality, and without it, rehabilitative interventions remain at best informed estimations of the underlying neural structures' response to produce movement. In this article, we utilize a novel modification to Procrustean distance to quantify curve dissimilarity and propose the Reach Severity and Dissimilarity Index (RSDI) as an objective measure of motor deficits. All experiments took place at the Medstar National Rehabilitation Hospital; persons with stroke were recruited from the hospital patient population. Using Fugl-Meyer (FM) scores and reach capacities, stroke survivors were placed in either mild or severe impairment groups. Individuals completed sets of reach-to-target tasks to extrapolate kinematic metrics describing motor performance. The Procrustes method of statistical shape analysis was modified to identify reaching sub-movements that were congruous to able-bodied sub-movements. Movement initiation proceeds comparably to the reference curve in both two- and three-dimensional representations of mild impairment movement. There were significant effects of the location of congruent segments between subject and reference curves, mean velocities, peak roll angle, and target error. These metrics were used to calculate a preliminary RSDI score with severity and dissimilarity sub-scores, and subjects were reclassified in terms of rehabilitation goals as Speed Emphasis, Strength Emphasis, and Combined Emphasis. The modified Procrustes method shows promise in identifying disruptions in movement and monitoring recovery without adding to patient or clinician burden. The proposed RSDI score can be adapted and expanded to other functional movements and used as an objective clinical tool. By reducing the impact of stroke on disability, there is a significant potential to improve quality of life through individualized rehabilitation.

Hand bone extraction and segmentation based on a convolutional neural network

Automatic segmentation of hand bone X-ray images is an important process in skeletal bone age assessment (BAA). The segmentation process is extremely difficult and complicated due to the complex structure and tiny size of hand bone features, which may lead to inaccurate feature localization and incomplete segmentation. A two-stage segmentation method is proposed in this paper. In the first stage, the OSA-YOLOv5 network is used to extract hand bones, and in the second stage, GRU-UNet is used to separate extracted hand bones. The hand bone X-ray dataset consists of a mixed dataset of 1000 left-hand bone X-ray images of adolescents from licenced local hospitals and 1000 left-hand bone X-ray images from the RSNA public dataset. The training efficiency of the OSA-YOLOv5 network is 28 % higher than that of the traditional network, and the proposed network exhibits with a higher extraction accuracy. The segmentation accuracy of the GRU-Unet segmentation model 14.70 % higher than that if Unet. The spatial sequence features with spatial rules are found to have a positive correlation in GRU-Unet. Experimental results show that the proposed segmentation method has good generalization and robustness, which provides a good foundation for subsequent BAA.

An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids

Cerebral organoids recapitulate the structure and function of the developing human brain in vitro, offering a large potential for personalized therapeutic strategies. The enormous growth of this research area over the past decade with its capability for clinical translation makes a non-invasive, automated analysis pipeline of organoids highly desirable. This work presents a novel non-invasive approach to monitor and analyze cerebral organoids over time using high-field magnetic resonance imaging and state-of-the-art tools for automated image analysis. Three specific objectives are addressed, (I) organoid segmentation to investigate organoid development over time, (II) global cysticity classification and (III) local cyst segmentation for organoid quality assessment. We show that organoid growth can be monitored reliably over time and cystic and non-cystic organoids can be separated with high accuracy, with on par or better performance compared to state-of-the-art tools applied to brightfield imaging. Local cyst segmentation is feasible but could be further improved in the future. Overall, these results highlight the potential of the pipeline for clinical application to larger-scale comparative organoid analysis.

The Effect of Water Below the Road Surface on Surface Distress Index (SDI) Values in the Road Condition Survey (RCS) Method

The results of the road damage assessment will reveal the Surface Distress Index (SDI) value according to the Bina Marga method (potholes, cracks and rutting). To determine the effect of subsurface water on road damage, geo-electrical measurements were carried out at road locations where it is estimated that there is subsurface water. At the location of the road segment, the damage value is then compared between the road with normal conditions SDI=aNx±ε and the road with water below the road surface. The results of the SDI value are then compared with the number of tracks of heavy vehicles (N) according to Equivalent Single Axle Load (ESAL) and the area of distribution of water below the surface (Aw). So it is expected to know the correlation model between road damage and the influence of water under the road surface surface SDI’=kAwn. These results will also be used as additional damage assessment items in the new damage assessment method based on the Bina Marga method.

Finite element modeling to predict the influence of anatomic variation and implant placement on performance of biological intervertebral disc implants.

Tissue-engineered intervertebral disc (TE-IVD) constructs are an attractive therapy for treating degenerative disc disease and have previously been investigated in vivo in both large and small animal models. The mechanical environment of the spine is notably challenging, in part due to its complex anatomy, and implants may require additional mechanical support to avoid failure in the early stages of implantation. As such, the design of suitable support implants requires rigorous validation. We created a FE model to simulate the behavior of the IVD cages under compression specific to the anatomy of the porcine cervical spine, validated the FE model using an animal model, and predicted the effects of implant location and vertebral angle of the motion segment on implant behavior. Specifically, we tested anatomical positioning of the superior vertebra and placement of the implant. We analyzed corresponding stress and strain distributions. Results demonstrated that the anatomical geometry of the porcine cervical spine led to concentrated stress and strain on the posterior side of the cage. This stress concentration was associated with the location of failure of the cages reported in vivo, despite superior mechanical properties of the implant. Furthermore, placement of the cage was found to have profound effects on migration, while the angle of the superior vertebra affected stress concentration of the cage. This model can be utilized both to inform surgical procedures and provide insight on future cage designs and can be adopted to models without the use of in vivo animal models.

A topology constrained phase field model

This paper presents a topology constrained phase field model based on a variable mobility Cahn-Hilliard equation. Mostly in the level set framework, this issue has been addressed using concepts from digital topology. However, point based specifications can not be effective in phase fields where the interface is represented by a diffuse region. In phase field models, topology constraints are included in the energy as a penalty term. However, there are no studies which provide topological constraints in the Cahn-Hilliard model to our knowledge. In this work, we use the mobility parameter to enforce topology constraints; this allows local, explicit control of topological events, unlike the optimization based solutions. We define topologically critical regions in phase fields based on a topological analysis of phase field evolution. This analysis shows that diffuse layers formed at topological encounters behave like a Morse function and the critical points of these transitional layers give topologically critical points in phase fields. Using this property, we first detect critical points using an efficient image analysis method. Then we detect the transitional region (critical region) around each critical point using a local morphological segmentation algorithm. We define a mobility function which both has a spatial and concentration dependence. This function is designed to reverse the effect of diffuse layers formed at topological encounters and prevent the further development of topological events. Being extracted from phase field values, the mobility map naturally aligns with phase field evolution. Together with the use of an unconditionally stable semi-implicit Fourier spectral method for the variable mobility Cahn-Hilliard equation, an efficient model is provided. Local, explicit control mechanism allows the design of free energy functional for modeling different materials; this extends the use of Cahn-Hilliard equation to applications which require topology control.

Multiple damage segmentation and extraction of wind turbine blades surface under complex background based on SKRT approach

Machine vision detection technology has been widely used in detecting wind turbine blade surface damage, but the complex background often has a significant impact on blade damage detection. Aiming at the complex background blade images caused by various natural background features, complex blade surface features, low contrast features, non-uniform illumination, weather conditions, and other factors, the work proposes an SKRT segmentation method based on K-means clustering and adaptive threshold fusion to segment and extract multiple damage features on the surface of wind turbine blades with complex backgrounds. Firstly, an single-scale retinex(SSR) algorithm is adopted to enhance the contrast between surface damage and image background in the research. Then the image is roughly segmented by K-means clustering to remove most of the background features, and the background mask is replaced with the peak of the remaining feature image pixels to solve the problem of wrong segmentation. Finally, making use of the adaptive threshold local segmentation method to accurately segment the blade surface damage. The experimental results show that the proposed SKRT segmentation method can significantly improve the segmentation accuracy of wind turbine blade surface damage with complex background, and the accuracy, intersection over union, and F-measure value are increased by 37.20%, 35.71%, and 28.69%, respectively. The method performs better robustness to multiple damage feature segmentation.

Deep Residual Networks for Image Recognition

Training deep neural networks is more challenging. We introduce a residual learning framework to simplify the training process for networks that are significantly deeper than those previously used. We specifically redesign the layers to learn residual functions based on the layer inputs, rather than learning functions without any reference. We present thorough empirical evidence indicating that residual networks are easier to optimize and can achieve higher accuracy with significantly increased depth. On the ImageNet dataset, we tested residual networks up to 152 layers deep, which are 8 times deeper than VGG networks but with lower complexity. An ensemble of these networks achieved a 3.57% error rate on the ImageNet test set, securing 1st place in the ILSVRC 2015 classification task. We also analyze networks with 100 and 1000 layers on CIFAR-10. The depth of representations is crucial for various visual recognition tasks. By utilizing extremely deep representations, we achieved a 28% relative improvement on the COCO object detection dataset. Deep residual networks were the basis of our submissions to the ILSVRC and COCO 2015 competitions, where we secured 1st place in ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation tasks.

Spatial asynchrony and cross-scale climate interactions in populations of a coldwater stream fish.

Climate change affects populations over broad geographic ranges due to spatially autocorrelated abiotic conditions known as the Moran effect. However, populations do not always respond to broad-scale environmental changes synchronously across a landscape. We combined multiple datasets for a retrospective analysis of time-series count data (5-28 annual samples per segment) at 144 stream segments dispersed over nearly 1,000 linear kilometers of range to characterize the population structure and scale of spatial synchrony across the southern native range of a coldwater stream fish (brook trout, Salvelinus fontinalis), which is sensitive to stream temperature and flow variations. Spatial synchrony differed by life stage and geographic region: it was stronger in the juvenile life stage than in the adult life stage and in the northern sub-region than in the southern sub-region. Spatial synchrony of trout populations extended to 100-200 km but was much weaker than that of climate variables such as temperature, precipitation, and stream flow. Early life stage abundance changed over time due to annual variation in summer temperature and winter and spring stream flow conditions. Climate effects on abundance differed between sub-regions and among local populations within sub-regions, indicating multiple cross-scale interactions where climate interacted with local habitat to generate only a modest pattern of population synchrony over space. Overall, our analysis showed higher degrees of response heterogeneity of local populations to climate variation and consequently population asynchrony than previously shown based on analysis of individual, geographically restricted datasets. This response heterogeneity indicates that certain local segments characterized by population asynchrony and resistance to climate variation could represent unique populations of this iconic native coldwater fish that warrant targeted conservation. Advancing the conservation of this species can include actions that identify such priority populations and incorporate them into landscape-level conservation planning. Our approach is applicable to other widespread aquatic species sensitive to climate change.

Non-destructive method for evaluating local integrity of model piles using electromagnetic waves

The objective of this study is to suggest a more accurate integrity estimation method in drilled shafts using electromagnetic waves propagating through transmission lines. Model piles, whose dimensions were 3.0 m in length, 0.2 m in height, and 0.2 m in width, were prepared. In the center of model piles along the length, the rebar and transmission line with 0, 1, 2, or 3 connectors were installed. In the middle of model piles, defect segments with length of 1.0 m were prepared The defect segments were filled with air, sands with different water contents, and water. The experimental results show that the velocity of the electromagnetic waves are dependent on the properties of the medium surrounding the transmission line. Although defects were detected in the transmission line without the connector, the location and size of defect were not clearly identified. However, for the transmission line with connectors, the location and size of sound and defect segments were more clearly identified using the velocity of electromagnetic waves determined between the connectors. This study suggests that transmission line with connectors may be effectively used for a more accurate evaluation of the integrity of drilled shafts.