Threshold Segmentation Method Research Articles

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.

Improved DeepLabV3+ Network Beacon Spot Capture Methods

Improved DeepLabV3+ Network Beacon Spot Capture Methods

A preliminary study on the identification of microcracks on the aggregate surface of asphalt pavements under cumulative tire wear

This paper presents a microcrack segmentation method and two-dimensional planar parameters to quantitatively characterize microcracks in aggregates under tire loading. First, an aggregate sample with extensive repeated tire wear was collected from the pavement surface for rock CT scanning. The acquired grayscale images were pre-processed by image filtering and appropriate image enhancement. Then, a microcrack segmentation method for aggregate damage analysis was developed based on the edge region growth algorithm and compared with the traditional threshold segmentation method. Finally, based on the microcrack segmentation results, the distribution pattern of microscopic characteristic parameters of aggregate surface microcracks was revealed. The results show that the best image pre-processing scheme is to first reduce the noise in the gray-scale images by using the Gaussian and median filters together, and then enhance their local details by gamma transform. Compared with the traditional threshold segmentation method, the proposed method effectively improves the microcrack segmentation accuracy by setting a large number of seed positions and adjusting their growth patterns. Moreover, the microscopic characteristic parameter distribution pattern of aggregate microcracks in each layer suggests that the crack extension degree on the pavement aggregate surface directly subjected to tire loads is generally larger than that on other parts. In addition, the microcracks are not only densely distributed within the aggregate surface layer, but also tend to extend deeper, indicating that the effect of cumulative tire loads on frictional damage to the pavement surface needs to be investigated in the field of pavement skid resistance research.

Interactive machine learning for segmenting pores of sandstone in computed tomography images

In the realm of gas field development and CO2/H2 geological storage, the precise characterization of reservoir parameters stands as a critical role. In recent years, digital rock technology has emerged as a cutting-edge tool for examining micro-pore structures, permeability parameter characteristics, and reservoir flow mechanisms. However, there are still certain defects in image segmentation, where low-density pore bodies in low-resolution images make it difficult to clearly distinguish between pores and solid minerals, affecting the accuracy of pore segmentation. This article aims to improve the accuracy of sandstone pore segmentation. We utilized the interactive machine learning segmentation method to segment pore structures and compare the segmented images obtained with the traditional ImageJ grayscale threshold segmentation methods. Furthermore, we evaluated the effectiveness of different segmentation methods quantitatively using the confusion matrix. The research results indicate that the segmentation quality (F1 score) of the machine learning method is higher than the grayscale methods, and the segmentation effectiveness of machine learning method is more stable. To further validate the accuracy of the machine learning method, we simulated the permeability and the results were compared with the experimental results, and the results show that the deviation in permeability segmented through machine learning methods is the smallest. Therefore, our results demonstrate the effective application of the machine learning method in segmenting sandstone pores in the digital rock images.

Preference aggregation method in determining brightness threshold values for object recognition on optical images

Relevance. The need to automate the processing large volumes of geophysical information extracted from images obtained through aerial photography or space remote sensing. The main stage of image processing for object recognition is segmentation. It consists in dividing the image into two homogeneous, clearly distinguishable non-overlapping areas with known boundaries. Threshold segmentation methods are simple but effective and therefore popular. Aim. To describe the experimental studies results of application of the robust method of interval fusion with preference aggregation previously developed by the authors for selecting threshold values when segmenting objects in optical images. Objects. Coastline images taken by Earth remote sensing satellites. Methods. The analyzed image is divided into equal bands. Based on the brightness histogram of each band, its characteristic brightness interval is determined. For the obtained brightness intervals, the fusion result is calculated using the interval fusion with preference aggregation method. The latter is used as a threshold brightness value when forming a segmented image. Results. The results of experimental studies of 100 satellite images of the coastline showed that the proposed method provided correct separation of land and sea regions in 84 images. And the traditional methods of maximum entropy, arithmetic averaging and Otsu showed correct results only in 27, 76 and 73 cases, respectively. Evaluations of such the metrics as Precision, Recall and deviation of the calculated threshold from a known correct value showed the dominance of the proposed method over other tested ones in terms of segmentation quality.

A Comprehensive Survey of Multi-Level Thresholding Segmentation Methods for Image Processing

A Comprehensive Survey of Multi-Level Thresholding Segmentation Methods for Image Processing

A New Classification Rule-Set for Mapping Surface Water in Complex Topographical Regions Using Sentinel-2 Imagery

Surface water is a critical natural resource, but its mapping accuracy is vulnerable to cloud cover, snow, shadows, and diverse roofing materials. Recognizing the limitations of a single threshold segmentation method that fails to achieve high-precision extraction of surface water in complex terrain areas, this study introduces a multiple threshold water detection rule (MTWDR) method to improve water extraction results. This method uses the multi-band reflectance characteristics of ground features to construct a water index and combines brightness features with the Otsu algorithm to eliminate interference from highly reflective ground features like ice, snow, bright material buildings, and clouds. The Yunan–Guizhou Plateau was selected as the study area due to its complex terrain and multiple types of surface water, and experiments were conducted using Sentinel-2 data on the Google Earth Engine (GEE). The results demonstrate that: (1) The proposed method achieves an overall accuracy of 94.08% and a kappa coefficient of 0.8831 in mountainous areas. In urban areas, the overall accuracy reaches 95.15%, accompanied by a kappa coefficient of 0.8945. (2) Compared to five widely used water indexes and rules, the MTWDR method improves accuracy by more than 3%. (3) It effectively overcomes interference from highly reflective ground features while maintaining the integrity and accuracy of water boundary extraction. In conclusion, the proposed method enhances extraction accuracy across different types of surface water within complex terrain areas, and can provide significant theoretical implications and practical value for researching and applying surface water resources.

Tunnel lining crack detection model based on improved YOLOv5

An improved real-time tunnel lining crack detection model based on YOLOv5 is proposed. This model maintains high precision and accuracy crack detection in low-light, low-contrast and high-noise environments by introducing several effective data augmentation techniques as well as semantic context encoding (SCE) and detail preserving encoding (DPE) at the head of the network structure. It achieves 90 % precision, 91 % recall, and 92 % mAP@50. The model demonstrates better detection performance than YOLOv4-tiny, YOLOv5s, YOLOv8s, and traditional threshold segmentation method, especially in complex environments to reduce misdetection and omission. The average detection time is only 12 ms per image, demonstrating the feasibility of its real-time application. The robust and generalization performance of the model is validated in specific engineering applications, showing great potential for improving detection efficiency, cost-effectiveness, and reliability in tunnel safety assessment and disasters management.

Use of the threshold segmentation method as an alternative for estimating the volume of forest industry residues

Use of the threshold segmentation method as an alternative for estimating the volume of forest industry residues

Quantification of depth and morphology of internal corrosion defects by stepped eddy current thermography skewness under weak excitation conditions

In the nondestructive testing (NDT) of eddy current thermography of oil and gas tanks, the low and non-uniform temperature rise due to weak excitation conditions makes the quantitative estimation of the depth and area of defects still challenging. This work proposes a mathematical-physical model of skewness to characterize defect depth quantitatively. The skewness image is estimated in Gaussian scale space using the background difference method combined with the chunked threshold segmentation method to characterize the defect morphology. The experimental results show that the skewness is affected by the combination of defect depth and area. As the defect area decreases, the relationship between defect depth and skewness gradually degrades from nonlinear to linear, while the rate of change of skewness becomes insignificant. The skewness analysis model predicted the maximum MRE of 5.15%, the minimum R2 of 0.94 for defect depth, and the MRE of 8.39% for defect area. It shows that the skewness can effectively characterize the depth and morphology of internal corrosion defects.

A rapid evaluation method of blasting effect based on optimized image segmentation algorithm and application in engineering

To quickly determine the blasting block degree and conduct an accurate and objective analysis of the tunnel blasting effect, this study has enhanced and improved upon the traditional genetic algorithm and Otsu algorithm. It has combined it with the marking watershed method and utilized ground digital acquisition to capture images of blasting debris. These images are then used in our custom-developed blasting analysis software to calculate the blasting block degree distribution and provide a quantitative analysis of blasting block degree. The research results show that the optimized image segmentation algorithm effectively improves the traditional threshold segmentation method on the poor effect of segmentation of the edge of the adherent block or the direct application of the watershed segmentation of the over-segmentation problem, to improve the segmentation accuracy based on the new segmentation technology is close to the traditional technology in terms of time. Through the self-developed software, the construction personnel in the project site to quickly obtain the blasting block degree histogram, block degree cumulative curve and other important indicators of the evaluation of the effect of blasting block degree, to provide data support for on-site construction, to assist in the modification of the blasting program, and to improve the efficiency of construction. This study realizes the rapid detection and block identification of blasting blocks, provides data support for the optimization of blasting parameters, and has good application and promotion value.

Low-Cost Plant-Protection Unmanned Ground Vehicle System for Variable Weeding Using Machine Vision.

This study presents a machine vision-based variable weeding system for plant- protection unmanned ground vehicles (UGVs) to address the issues of pesticide waste and environmental pollution that are readily caused by traditional spraying agricultural machinery. The system utilizes fuzzy rules to achieve adaptive modification of the Kp, Ki, and Kd adjustment parameters of the PID control algorithm and combines them with an interleaved period PWM controller to reduce the impact of nonlinear variations in water pressure on the performance of the system, and to improve the stability and control accuracy of the system. After testing various image threshold segmentation and image graying algorithms, the normalized super green algorithm (2G-R-B) and the fast iterative threshold segmentation method were adopted as the best combination. This combination effectively distinguished between the vegetation and the background, and thus improved the accuracy of the pixel extraction algorithm for vegetation distribution. The results of orthogonal testing by selected four representative spraying duty cycles-25%, 50%, 75%, and 100%-showed that the pressure variation was less than 0.05 MPa, the average spraying error was less than 2%, and the highest error was less than 5% throughout the test. Finally, the performance of the system was comprehensively evaluated through field trials. The evaluation showed that the system was able to adjust the corresponding spraying volume in real time according to the vegetation distribution under the decision-making based on machine vision algorithms, which proved the low cost and effectiveness of the designed variable weed control system.

Adaptive Segmentation Algorithm for Subtle Defect Images on the Surface of Magnetic Ring Using 2D-Gabor Filter Bank

In order to realize the unsupervised segmentation of subtle defect images on the surface of small magnetic rings and improve the segmentation accuracy and computational efficiency, here, an adaptive threshold segmentation method is proposed based on the improved multi-scale and multi-directional 2D-Gabor filter bank. Firstly, the improved multi-scale and multi-directional 2D-Gabor filter bank was used to filter and reduce the noise on the defect image, suppress the noise pollution inside the target area and the background area, and enhance the difference between the magnetic ring defect and the background. Secondly, this study analyzed the grayscale statistical characteristics of the processed image; the segmentation threshold was constructed according to the gray statistical law of the image; and the adaptive segmentation of subtle defect images on the surface of small magnetic rings was realized. Finally, a classifier based on a BP neural network is designed to classify the scar images and crack images determined by different threshold segmentation methods. The classification accuracies of the iterative method, the OTSU method, the maximum entropy method, and the adaptive threshold segmentation method are, respectively, 85%, 87.5%, 95%, and 97.5%. The adaptive threshold segmentation method proposed in this paper has the highest classification accuracy. Through verification and comparison, the proposed algorithm can segment defects quickly and accurately and suppress noise interference effectively. It is better than other traditional image threshold segmentation methods, validated by both segmentation accuracy and computational efficiency. At the same time, the real-time performance of our algorithm was performed on the advanced SEED-DVS8168 platform.

Recognition and position estimation method for stacked untextured parts

To address the challenge of recognizing and estimating the position of untextured stacked parts, which are common in industrial environments, this study proposes an integrated approach that incorporates the YOLOv7 target detection algorithm and point cloud alignment techniques. First, the YOLOv7 algorithm is utilized to quickly identify and locate the 2D position of the part, followed by a mapping technique to transform the 2D region of interest (ROI) into the corresponding 3D point cloud region. In the point cloud processing stage, depth threshold segmentation and Euclidean clustering segmentation methods are used to separate the target part from the background and other interfering objects. The pose estimation stage uses the SAC-IA algorithm for coarse alignment, followed by an improved ICP algorithm that introduces an adaptive weighting mechanism and a global optimization strategy for fine alignment to obtain the final 6D pose of the part. The improved strategy significantly optimizes the point-pair selection and alignment process and enhances the robustness and accuracy of the algorithm. Through experimental validation on publicly available part piece datasets, the results show that the part identification and pose estimation method proposed in this study can realize fast and accurate identification and pose estimation of different shapes, non-textured, and scattered stacked parts, where the position error can reach up to 1mm and the angular error within 1°, which meets the requirements of practical applications.

Threshold segmentation based on information fusion for object shadow detection in remote sensing images

In the shadow detection task, the shadow model is usually consistent with the approximate contour of ontology semantics, it is difficult to extract the features of land covered objects or ground pixels, and easy to be confused into foreground objects in gray scale. Therefore, we present to formulate and apply one new threshold segmentation method based on information fusion for object shadow detection in remote sensing images. Firstly, object shadow pixels are screened using intensity and chromaticity information in HSI color space. Secondly, the remote sensing image is carried out by principal component analysis (PCA) to obtain the first principal component. A new shadow index is constructed using the results obtained from HSI and the first principal component. Thirdly, based on the results of the above two information fusion, a threshold segmentation model is established using the improved threshold segmentation algorithm between the maximum and the minimum threshold segmentation algorithm, so as to obtain the final object shadow detection results. Finally, affluent experiments are conducted on the datasets collected from Google Earth. The results show that the proposed object shadow detection algorithm in remote sensing images can achieve better segmentation and detection (more than 95%) effect compared with state-of-the-art methods.

In Situ Root Dataset Expansion Strategy Based on an Improved CycleGAN Generator.

The root system plays a vital role in plants' ability to absorb water and nutrients. Insitu root research offers an intuitive approach to exploring root phenotypes and their dynamics. Deep-learning-based root segmentation methods have gained popularity, but they require large labeled datasets for training. This paper presents an expansion method for insitu root datasets using an improved CycleGAN generator. In addition, spatial-coordinate-based target background separation method is proposed, which solves the issue of background pixel variations caused by generator errors. Compared to traditional threshold segmentation methods, this approach demonstrates superior speed, accuracy, and stability. Moreover, through time-division soil image acquisition, diverse culture medium can be replaced in insitu root images, thereby enhancing dataset versatility. After validating the performance of the Improved_UNet network on the augmented dataset, the optimal results show a 0.63% increase in mean intersection over union, 0.41% in F1, and 0.04% in accuracy. In terms of generalization performance, the optimal results show a 33.6% increase in mean intersection over union, 28.11% in F1, and 2.62% in accuracy. The experimental results confirm the feasibility and practicality of the proposed dataset augmentation strategy. In the future, we plan to combine normal mapping with rendering software to achieve more accurate shading simulations of insitu roots. In addition, we aim to create a broader range of images that encompass various crop varieties and soil types.

Research on Weakly Supervised Pavement Crack Segmentation Based on Defect Location by Generative Adversarial Network and Target Re‐optimization

This paper presents a weakly supervised method of pavement crack segmentation. The U-GAT-IT model is trained by image-level labels to obtain the class activation map (CAM). Then, by erasing the cracks that have been detected, the image is re-detected to form a more complete CAM. The pseudo-labels obtained by using the adaptive threshold segmentation method are used to train U2-Net, and the trained U2-Net is used to optimize the pseudo-labels to train U2-Net with better quality. Finally, the result shows that the method effectively narrows the gap between unsupervision and full supervision of pavement cracks.

Inter-software and inter-threshold reliability of quantitative paraspinal muscle segmentation

PurposeChanges in the cross-sectional area (CSA) and functional cross-sectional area (FCSA) of the lumbar multifidus (MF) and erector spinae muscles (ES) are factors that can contribute to low back pain. For the assessment of muscle CSA and composition there are various software and threshold methods used for tissue segmentation in quantitative analysis. However, there is currently no gold standard for software as well as muscle segmentation. This study aims to analyze the measurement error between different image processing software and different threshold methods for muscle segmentation.MethodsMagnetic resonance images (MRI) of 60 patients were evaluated. Muscle CSA and FCSA measurements were acquired from axial T2-weighted MRI of the MF and ES at L4/L5 and L5/S1. CSA, FCSA, and FCSA/CSA ratio were measured independently by two observers. The MRI images were measured using two different software programs (ImageJ and Amira) and with two threshold methods (Circle/Overlap method) for each software to evaluate FCSA and FCSA/CSA ratio.ResultsInter-software comparisons revealed high inter-rater reliability. However, poor inter-rater reliability were obtained with different threshold methods. CSA, FCSA, and FCSA/CSA showed excellent inter-software agreement of 0.75–0.99 regardless of the threshold segmentation method. The inter-rater reliability between the two observers ranged between 0.75 and 0.99. Comparison of the two segmentation methods revealed agreement between 0.19 and 0.84. FCSA and FCSA/CSA measured via the Overlap method were significantly higher than those measured via the Circle method (P < 0.01).ConclusionThe present study showed a high degree of reliability with very good agreement between the two software programs. However, study results based on different threshold methods should not be directly compared.

Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features

In infrared images of the sea surface, apart from the complex background of the sea surface, there are often sky and island backgrounds. The disturbances caused by sea wind and the reflection of intense sunlight on the sea surface increase the complexity of the background, which seriously hinders the detection of targets. To achieve the detection of dark-polarity ship targets in such environments, a dual-channel threshold segmentation method based on local low-gray region detection and geometric features judgment is proposed in this paper. In one channel, adaptive threshold segmentation is performed on the low-gray regions of the acquired image and combined with geometric features to obtain a finer segmentation result. In the other channel, adaptive segmentation is performed on the preprocessed image, and potential backgrounds that may be finely segmented as targets are filtered out based on an area threshold. Finally, the results of the two channels are multiplied and fused to obtain an accurate segmentation result. Experimental results demonstrate that the proposed algorithm outperforms the comparison algorithm in subjective and objective evaluations. The proposed algorithm in this paper not only achieves a low false alarm rate but also exhibits a higher detection rate, and the average detection rate in the test sequence surpasses 95%.

Research on Texture Feature Recognition of Regional Architecture Based on Visual Saliency Model

Architecture is a representative of a city. It is also a spatial carrier of urban culture. Identifying the architectural features in a city can help with urban transformation and promote urban development. The use of visual saliency models in regional architectural texture recognition can effectively enhance the effectiveness of regional architectural texture recognition. In this paper, the improved visual saliency model first enhances the texture images of regional buildings through histogram enhancement technology, and uses visual saliency algorithms to extract the visual saliency of the texture features of regional buildings. Then, combined with the maximum interclass difference method of threshold segmentation, the visual saliency image is segmented to achieve accurate target recognition. Finally, the feature factor iteration of the Bag of Visual Words model and the function classification of support vector machines were used to complete the recognition of regional architectural texture features. Through experimental verification, the constructed regional architectural texture feature recognition method based on visual saliency model can effectively enhance the recognition image. This method performs well in boundary contour separation and visual saliency, with an average recognition rate of 0.814 for texture features in different building scenes, indicating high stability.