Segmentation Algorithm Research Articles

GSRPSO: A multi-strategy integrated particle swarm algorithm for multi-threshold segmentation of real cervical cancer images

Cervical cancer is one of the most common gynecological malignant tumors and the fourth largest malignant tumor endangering the life and health of women worldwide. The lesion areas of cervical cancer usually show complexity and diversity. To distinguish different cervical lesion sites and assist doctors in diagnosis and decision-making, this paper first proposes a multi-strategy integrated particle swarm optimization algorithm (GSRPSO, for short). The four strategies in GSRPSO work together to improve its optimization capabilities. Among them, the dynamic parameters balance the exploration and exploitation phases. The gaining sharing strategy and the random position updating strategy accelerate the convergence process while enhancing the diversity of the population. The vertical crossover mutation strategy improves local exploitation and avoids premature stagnation of the algorithm. The optimization performance of GSRPSO is validated by comparison experiments with 15 state-of-the-art algorithms on the CEC2020 test set. In addition, we established a MIS method based on the GSRPSO algorithm by combining the non-local mean algorithm and 2D Kapur entropy. This method is compared with the MIS methods combining 2D Renyi entropy, 2D Tsallis entropy, and 2D Masi entropy in a comparison experiment with four sets of thresholds on six BSDS500 images. The experimental results show that this MIS method exhibits obvious advantages in segmentation quality and stability. Finally, nine cervical cancer images are segmented using the GSRPSO-based MIS method, and experiments are conducted with nine excellent optimization algorithms under six different sets of thresholds. The experimental results show that this MIS method demonstrates better segmentation quality and accuracy than similar methods with the optimal evaluation indexes PSNR=28.3645, SSIM=0.8996, FSIM=0.9494, AD=8.1939, and NAE=0.0710. In conclusion, the GSRPSO-based MIS method is one of a class of highly promising methods to assist physicians in accurately diagnosing cervical cancer.

An adaptive enhanced human memory algorithm for multi-level image segmentation for pathological lung cancer images

Lung cancer is a critical health issue that demands swift and accurate diagnosis for effective treatment. In medical imaging, segmentation is crucial for identifying and isolating regions of interest, which is essential for precise diagnosis and treatment planning. Traditional metaheuristic-based segmentation methods often struggle with slow convergence speed, poor optimized thresholds results, balancing exploration and exploitation, leading to suboptimal performance in the multi-thresholding segmenting of lung cancer images. This study presents ASG-HMO, an enhanced variant of the Human Memory Optimization (HMO) algorithm, selected for its simplicity, versatility, and minimal parameters. Although HMO has never been applied to multi-thresholding image segmentation, its characteristics make it ideal to improve pathology lung cancer image segmentation.The ASG-HMO incorporating four innovative strategies that address key challenges in the segmentation process. Firstly, the enhanced adaptive mutualism phase is proposed to balance exploration and exploitation to accurately delineate tumor boundaries without getting trapped in suboptimal solutions. Second, the spiral motion strategy is utilized to adaptively refines segmentation solutions by focusing on both the overall lung structure and the intricate tumor details. Third, the gaussian mutation strategy introduces diversity in the search process, enabling the exploration of a broader range of segmentation thresholds to enhance the accuracy of segmented regions. Finally, the adaptive t-distribution disturbance strategy is proposed to help the algorithm avoid local optima and refine segmentation in later stages.The effectiveness of ASG-HMO is validated through rigorous testing on the IEEE CEC'17 and CEC'20 benchmark suites, followed by its application to multilevel thresholding segmentation in nine histopathology lung cancer images. In these experiments, six different segmentation thresholds were tested, and the algorithm was compared to several classical, recent, and advanced segmentation algorithms. In addition, the proposed ASG-HMO leverages 2D Renyi entropy and 2D histograms to enhance the precision of the segmentation process.Quantitative result analysis in pathological lung cancer segmentation showed that ASG-HMO achieved superior maximum Peak Signal-to-Noise Ratio (PSNR) of 31.924, Structural Similarity Index Measure (SSIM) of 0.919, Feature Similarity Index Measure (FSIM) of 0.990, and Probability Rand Index (PRI) of 0.924. These results indicate that ASG-HMO significantly outperforms existing algorithms in both convergence speed and segmentation accuracy. This demonstrates the robustness of ASG-HMO as a framework for precise segmentation of pathological lung cancer images, offering substantial potential for improving clinical diagnostic processes.

A clinical-radiomics nomogram based on automated segmentation of chest CT to discriminate PRISm and COPD patients

PurposeIt is vital to develop noninvasive approaches with high accuracy to discriminate the preserved ratio impaired spirometry (PRISm) group from the chronic obstructive pulmonary disease (COPD) groups. Radiomics has emerged as an image analysis technique. This study aims to develop and confirm the new radiomics-based noninvasive approach to discriminate these two groups. MethodsTotally 1066 subjects from 4 centers were included in this retrospective research, and classified into training, internal validation or external validation sets. The chest computed tomography (CT) images were segmented by the fully automated deep learning segmentation algorithm (Unet231) for radiomics feature extraction. We established the radiomics signature (Rad-score) using the least absolute shrinkage and selection operator algorithm, then conducted ten-fold cross-validation using the training set. Last, we constructed a radiomics signature by incorporating independent risk factors using the multivariate logistic regression model. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA). ResultsThe Rad-score, including 15 radiomic features in whole-lung region, which was suitable for diffuse lung diseases, was demonstrated to be effective for discriminating between PRISm and COPD. Its diagnostic accuracy was improved through integrating Rad-score with a clinical model, and the area under the ROC (AUC) were 0.82(95 %CI 0.79–0.86), 0.77(95 %CI 0.72–0.83) and 0.841(95 %CI 0.78–0.91) for training, internal validation and external validation sets, respectively. As revealed by analysis, radiomics nomogram showed good fit and superior clinical utility. ConclusionsThe present work constructed the new radiomics-based nomogram and verified its reliability for discriminating between PRISm and COPD.

Spatial-spectral feature mining in hyperspectral corn leaf venation structure and its application in nitrogen content estimation

The growth of corn plants requires a significant amount of nitrogen nutrient supply to ensure the yield. Meanwhile, overfertilization could result in adverse environmental impacts, making it crucial to monitor the nitrogen supply condition in corn plants precisely. Hyperspectral imaging (HSI) could be the solution to this challenge, as HSI plant phenotyping technology has been widely utilized to nondestructively identify plant traits, such as mineral nutrient deficiencies in corn plants. However, limitations persist where the conventional HSI nitrogen prediction models mainly relied on the overall colors of the plant tissue extracted from the spectral domain but rarely included the spatially distributed information on the leaf level. This study aimed to examine if including new information from the spatial domain could potentially improve the performance of HSI models. Because the venation structure plays a crucial role in the distribution of various nutrients across the leaf, a series of physiological responses caused by nitrogen deficiency could result in non-uniform color patterns related to the veins. The recent advancement of HSI techniques such as LeafSpec, whose imaging quality and spatial-spectral resolution have been significantly improved, made it possible to extract the venation structure together with high-resolution spatial-spectral features. In this study, the hypothesis was that utilizing high-resolution spatial-spectral features extracted based on the venation structure could yield a better-performed nitrogen content prediction model compared to the averaged spectrum of the corn leaf. First, a venation structure segmentation algorithm was developed using the Local Binary Patterns (LBP) method for hyperspectral corn leaf images scanned with LeafSpec. Second, for every hyperspectral leaf image, 131 spectral index heatmaps were generated, and 30 leaf regions were picked based on the venation structure, resulting in 3930 spatial-spectral features. Third, the features were picked using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. As the results tested with a field assay involving two different corn genotypes and two nitrogen treatments, the Partial Least Square Regression (PLS-R) model built with selected spatial-spectral features outperformed the model built with the averaged leaf spectra in terms of the Root Mean Squared Error (RMSE) of predictions. The results provided encouraging proof and guidance for the potential benefit of using high-resolution spatial-spectral features derived from hyperspectral leaf images to improve the accuracy and robustness of the nitrogen content prediction models for corn plants.

Augmented and Virtual Reality Based Segmentation Algorithm for Human Pose Detection in Wearable Cameras

Pose graph optimisation is a crucial method that helps reduce cumulative errors while estimating visual trajectories for wearable cameras. However, when the posture graph's size increases with each additional camera movement, the optimization's efficiency diminishes. In terms of ongoing sensitive applications, such as extended reality and computer-generated reality, direction assessment is a major test. This research proposes an incremental pose graph segmentation technique that accounts for camera orientation variations as a solution to this challenge. The computation only improves the cameras that have seen large direction changes by breaking the posture chart during these instances. As a result, pose graph optimisation is essentially slowed down and optimised more quickly. For every camera that hasn't been optimised using a pose graph, the algorithm employs the wearable cameras at the start and end of each camera's trajectory segment. The final camera in attendance is then determined by weighted average the various postures evaluated with these wearable cameras; this eliminates the need for lengthy nonlinear enhancement computations, reduces disturbance, and achieves excellent accuracy. Experiments on the EuRoC, TUM, and KITTI datasets demonstrate that pose graph optimisation scope is reduced while maintaining camera trajectories accuracy.

Optimisation of semantic segmentation algorithm for autonomous driving using U-NET architecture

In autonomous driving systems, the semantic segmentation task involves scene partition into numerous expressive portions by classifying and labelling every image pixel for semantics. The algorithm used for semantic segmentation has a vital role in autonomous driving architecture. This paper's main contribution is optimising the semantic segmentation algorithm for autonomous driving by modifying the U-NET architecture. The optimisation techniques involve five different methods, which include; no batch normalisation network, with batch normalisation network, network with reduction in filters, average ensemble network, and weighted average ensemble network. The validation accuracy observed for the five methods were 90.28%, 91.68%, 89.80%, 92.04%, and 92.21% respectively. By reducing the filters in the network, the computation time reduces (Epoch time: 1 s 64 ms/step) as opposed to the typical (Epoch time: 4 s 260 ms/step), but the accuracy reduces. The optimisation techniques were evaluated for metrics like mean intersection over union (IoU), IoU for class, dice-metric, dice_coefficient_loss, validation loss, and accuracy. The dataset of 300 images used for this paper's study was generated using the open-source car learning to act (CARLA) simulator platform.

Leukocyte segmentation based on DenseREU-Net

The detection of white blood cells provides important information in cellular research regarding infections, inflammation, immune function, and blood pathologies. Effective segmentation of WBCs in blood microscopic images not only aids pathologists in making more accurate diagnoses and early detections but is also crucial for identifying the types of lesions. Due to significant differences among various types of pathological WBCs and the complexities associated with cellular imaging and staining techniques, accurately recognizing and segmenting these different types of WBCs remains challenging. To address these challenges, this paper proposes a WBC segmentation technique based on DenseREU-Net, which enhances feature exchange and reuse by employing Dense Blocks and residual units. Additionally, it introduces mixed pooling and skip multi-scale fusion modules to improve the recognition and segmentation accuracy of different types of pathological WBCs. This study was validated on two datasets provided by DML-LZWH (Liuzhou Workers’ Hospital Medical Laboratory). Experimental results indicate that on the multi-class dataset, DenseREU-Net achieves an average IoU of 73.05% and a Dice coefficient of 80.25%. For the binary classification blind sample dataset, the average IoU and Dice coefficient are 83.98% and 90.41%, respectively. In both datasets, the proposed model significantly outperforms other advanced medical image segmentation algorithms. Overall, DenseREU-Net effectively analyzes blood microscopic images and accurately recognizes and segments different types of WBCs, providing robust support for the diagnosis of blood-related diseases.

A novel automated labelling algorithm for deep learning-based built-up areas extraction using nighttime lighting data

The use of remote sensing imagery and cutting-edge deep learning techniques can produce impressive results when it comes to built-up areas extraction (BUAE). However, reducing the manual labelling set production process while ensuring high accuracy is currently the main research topic. This study pioneers the exploitation of nighttime lighting data (NLD) for automatically generating deep learning label sets, assessing the feasibility, and identifying limitations of using varied intensity ranges of lighting data directly for this purpose. We provide a novel method for generating fine-grained labels through an optimisation technique that eliminates the necessity for human involvement. This approach employs deep learning segmentation algorithms and has been tested in eight cities across seven countries. The results indicate that segmentation performs well in most cities, with the combination of iso clustering and NLD allowing for more precise extraction of urban building districts. The overall accuracy exceeds 90% in most cities. The results based on manual and historical data (around 0.7) as labels are significantly lower than those based on NLD. At the same time, the segmentation effect of deep learning has a more significant advantage over traditional machine learning classification algorithms (around 0.8). DeeplabV3 and U-Net exhibit different strengths in segmentation and extraction: DeeplabV3 has a stronger ability to eliminate errors, while U-Net retains the capability to handle less labelled information, making them mutually advantageous depending on the specific requirements of the task. It proposes a Strategy to automatically extract built-up areas with minimal human involvement.

The Lightweight Fracture Segmentation Algorithm for Logging Images Based on Fully 3D Attention Mechanism and Deformable Convolution

The challenge of fracture segmentation remains a significant obstacle in imaging logging interpretation within the current oil and gas exploration and development field. However, existing image segmentation algorithms still encounter issues related to accuracy, speed, and robustness, as well as a tendency to misdetect or overlook small fractures when applied to logging image fracture segmentation tasks. To address these challenges comprehensively, this paper proposes an end-to-end fracture segmentation algorithm named SWSDS-Net. This algorithm is built upon the UNet architecture and incorporates the SimAM with slicing (SWS) attention mechanism along with the deformable strip convolution (DSCN) module. The SWS introduces a fully 3D attention mechanism that effectively learns the weights of each neuron in the feature map, enabling better capture of fracture features while ensuring fair attention and enhancement for both large and small objects. Additionally, the deformable properties of DSCN allow for adaptive sampling based on fracture shapes, effectively tackling challenges posed by varying fracture shapes and enhancing segmentation robustness. Experimental results demonstrate that SWSDS-Net achieves optimal performance across all evaluation metrics in this task, delivering superior visual results in fracture segmentation while successfully overcoming limitations present in existing algorithms such as complex shapes, noise interference, and low-quality images. Moreover, serving as a lightweight network solution enables SWSDS-Net’s deployment on mobile devices at remote sites—an advancement that lays a solid foundation for interpreting logging data and promotes deep learning technology application within traditional industrial scenarios.

Deep Learning-Based Postoperative Glioblastoma Segmentation and Extent of Resection Evaluation: Development, External Validation, and Model Comparison

Abstract Background The pursuit of automated methods to assess the extent of resection (EOR) in glioblastomas is challenging, requiring precise measurement of residual tumor volume. Many algorithms focus on preoperative scans, making them unsuitable for postoperative studies. Our objective was to develop a deep learning-based model for postoperative segmentation using magnetic resonance imaging (MRI). We also compared our model's performance with other available algorithms. Methods To develop the segmentation model, a training cohort from three research institutions and three public databases was used. Multiparametric MRI scans with ground truth labels for contrast enhancing tumor, edema, and surgical cavity, served as training data. The models were trained using MONAI and nnU-Net frameworks. Comparisons were made with currently available segmentation models using an external cohort from a research institution and a public database. Additionally, the model's ability to classify EOR was evaluated using the RANO-Resect classification system. To further validate our best-trained model, an additional independent cohort was used. Results The study included 586 scans: 395 for model training, 52 for model comparison, and 139 scans for independent validation. The nnU-Net framework produced the best model with median Dice scores of 0.81 for contrast enhancing tumor, 0.77 for edema, and 0.81 for surgical cavity. Our best-trained model classified patients into maximal and submaximal resection categories with 96% accuracy in the model comparison dataset and 84% in the independent validation cohort. Conclusion Our nnU-Net-based model outperformed other algorithms in both segmentation and EOR classification tasks, providing a freely accessible tool with promising clinical applicability.

Multi-Level Thresholding Color Image Segmentation Using Modified Gray Wolf Optimizer

The success of image segmentation is mainly dependent on the optimal choice of thresholds. Compared to bi-level thresholding, multi-level thresholding is a more time-consuming process, so this paper utilizes the gray wolf optimizer (GWO) algorithm to address this issue and enhance accuracy. To acquire the optimal thresholds at different levels, we modify the GWO (MGWO) in terms of leader selection, position update, and mutation. We also use the Otsu method and Kapur entropy as objective functions. The performance of MGWO is compared with other color image segmentation algorithms on ten images from the BSD500 dataset in terms of objective values, variance, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental and non-parametric statistical analyses demonstrate that MGWO performs excellently in the multi-level thresholding segmentation of color images.

A Helium Speech Correction Method Based on Generative Adversarial Networks

The distortion of helium speech caused by helium−oxygen gas mixtures significantly impacts the safety and communication efficiency of saturation divers. Although existing correction methods have shown some effectiveness in improving the intelligibility of helium speech, challenges remain in enhancing clarity and high−pitch correction. To address the issue of degraded speech quality post−correction, a novel helium speech correction method based on generative adversarial networks (GANs) is proposed. Firstly, a new helium speech dataset is introduced, which includes isolated words and continuous speech in both Chinese and English. By training and testing on both isolated words and continuous passages, the correction capability of the model can be accurately evaluated. Secondly, a new evaluation system for helium speech correction is proposed, which partially fills the gap in current helium speech evaluation metrics. This system uses comprehensive similarity to evaluate the similarity of keywords at the sentence level, thus assessing the correction results of helium speech from both word and sentence dimensions. Lastly, a GAN−based helium speech correction method is designed. This method solves the problems of pitch period distortion and formant shift in helium speech by introducing an adaptive speech segmentation algorithm and a fusion loss function and significantly improves the clarity and intelligibility of corrected helium speech. The experimental results show that the corrected helium speech is improved in clarity and intelligibility, which shows its practical value and application potential.

TIA-UNet: transformer-enhanced deep learning for adolescent idiopathic scoliosis spinal x-ray image segmentation

Abstract Adolescent Idiopathic Scoliosis (AIS) is a common spinal deformity where precise diagnosis is crucial for developing effective treatment strategies. Traditional manual x-ray image analysis is time-consuming and highly dependent on the operator’s expertise, thus constraining diagnostic efficiency and accuracy. This study aimed to develop an automated thoracolumbar spine segmentation method utilizing deep learning to enhance the efficiency and accuracy of AIS diagnosis. We introduced TIA-UNet, an innovative network architecture that combines Convolutional Neural Networks (CNN) with Transformer models. By integrating the IR Block and DA Block, TIA-UNet was optimized for feature extraction and multi-scale information fusion. The model underwent training and validation using our established Adolescent Scoliosis Medical Dataset (ASMD). TIA-UNet attained a Dice similarity coefficient of 90. 02%, a Mean Intersection over Union (MIoU) of 81. 96%, and a Hausdorff Distance (HD) of 4. 09, significantly surpassing current state-of-the-art methods such as UNet and TransUNet. Moreover, TIA-UNet exhibited superior computational efficiency regarding parameter count, inference time, and floating-point operations (FLOPs). As an automated medical image segmentation algorithm, TIA-UNet enhanced segmentation accuracy while maintaining high computational efficiency, demonstrating significant potential for clinical diagnostic applications. This study provides compelling evidence supporting the utilization of deep learning techniques in medical image analysis.

Measuring Cell Dimensions in Fission Yeast Using Machine Learning.

In fission yeast (Schizosaccharomyces pombe), cell length is a crucial indicator of cell cycle progression. Microscopy screens that examine the effect of agents or genotypes suspected of altering genomic or metabolic stability and thus cell size are crucial for studying disruptions to cell cycle dynamics. This method is based on using an automated cell segmentation algorithm to measure S. pombe cells imaged by brightfield (BF) microscopy methods. PhotoPhenosizer (PP) is a machine learning-based tool designed for automated cell measuring and dimensional analysis of morphology frequency distributions. Integration of this method into large-scale pipelines for tracking cell dimension change streamlines morphological measurements, which facilitates the examination of cellular responses to genomic and metabolic stresses. In this protocol, we use PP to observe the effect of genomic instability on cell size dynamics over a 12-day chronological lifespan assay. Our results show that relative to wild-type cells, a replication stress mutant shows larger cells during chronological aging in excess glucose media. Our results are consistent with activation of checkpoints that regulate cell morphology in response to DNA damage. This method's application highlights the relevance of its incorporation in experimental routines that require large-scale image processing and its adoption by users with routine needs in S. pombe molecular research projects.

YOLOv8-seg-CP: a lightweight instance segmentation algorithm for chip pad based on improved YOLOv8-seg model.

Real-time detection and accurate segmentation of chip pads are important tasks to ensure chip alignment and position correction. To address the challenges of small target chip pad detection, segmentation accuracy and model lightweight, this paper proposes a lightweight chip pad instance segmentation algorithm based on an improved YOLOv8-seg, named YOLOv8-seg-CP (chip pad). Firstly, we integrate the next-generation lightweight StarNet into the original backbone network to enhance fine feature capture capabilities while reducing the number of parameters. Then, we construct the C2f-Star module in the neck network, which enhances the feature extraction performance for small chip pad targets. This maintains accuracy, reduces computational load, and improves detection and segmentation speed. On this basis, we introduce a lightweight shared convolution segmentation head (LSCSH), significantly reducing both parameter count and computational load while enhancing segmentation performance. Additionally, we propose a CGCAFusion convolutional attention fusion module. This module uses a content-guided convolutional attention fusion mechanism to dynamically adjust attention weights based on the content of input features, capturing both global and local feature information and enhancing multimodal feature fusion. Experiments on the chip pad dataset demonstrate that our algorithm achieves a detection and segmentation accuracy of 89.8%. The model size, parameters, and FLOPs are 3.7M, 1.7M, and 8.2G respectively, representing reductions of 45.6%, 50%, and 31.7% compared to the baseline model. The FPS is 1399.3, an improvement of 25.8% over the baseline model. The inference time is 0.72ms, which is 0.18ms less than the baseline model. Extensive experimental results on COCO, carparts-seg, and crack-seg datasets further show that the improved YOLOv8n-seg model outperforms many existing advanced methods in terms of performance. This approach holds significant industrial application value for fully automated chip testing and sorting integrated production.

Art Pattern Design Based on Visual Semantic Segmentation and Evolutionary Algorithm Optimization

This paper aims to address the issue of traditional art patterns not fully leveraging contextual information in image semantic segmentation. It proposes a multi-level feature aggregation method for image semantic segmentation based on visual Transformer and coordinate attention adjustment (CA Adjustment). Initially, the input image is segmented into slices for linear projection, incorporating learnable positional embeddings to derive an encoded input sequence. Subsequently, employing a visual Transformer-based encoder, the image is transformed into patches to capture global context across the network. CA Adjustment is then introduced to adaptively merge fusion features and visual backbone features using weighted adjustments. Lastly, a multi-objective grasshopper optimization algorithm is designed based on a clustering evolution mechanism to further enhance algorithm performance. Extensive experimental results demonstrate that our proposed algorithm effectively models global contextual information for image feature extraction. Compared to existing advanced algorithms, our method achieves high segmentation accuracy in semantic segmentation tasks, consistently exceeding 95%. Ablation experiments further validate the efficacy of our approach, underscoring its broad applicability in high-precision image semantic segmentation.

SFNet: Spatial and Frequency Domain Networks for Wide-Field OCT Angiography Retinal Vessel Segmentation.

Automatic segmentation of blood vessels in fundus images is important to assist ophthalmologists in diagnosis. However, automatic segmentation for Optical Coherence Tomography Angiography (OCTA) blood vessels has not been fully investigated due to various difficulties, such as vessel complexity. In addition, there are only a few publicly available OCTA image data sets for training and validating segmentation algorithms. To address these issues, we constructed a wild-field retinal OCTA segmentation data set, the Retinal Vessels Images in OCTA (REVIO) dataset. Second, we propose a new retinal vessel segmentation network based on spatial and frequency domain networks (SFNet). The proposed model are tested on three benchmark data sets including REVIO, ROSE and OCTA-500. The experimental results show superior performance on segmentation tasks compared to the representative methods.

Method for Non-Contact Measuring the Weight of Sturgeon in Intensive Aquaculture

Weight information plays a pivotal role in sturgeon breeding and production management. However, manual measurement is time consuming and labor intensive due to the immense size of the sturgeon. Due to the unique body shape of the sturgeon, traditional image segmentation algorithms struggle to extract the necessary features from sturgeon images, which makes them unsuitable for this particular species. Moreover, accurately measuring weight in an occlusion environment is difficult. To address these challenges, an improved YOLOv5s model with a context augmentation module, focal-efficient intersection over union, and soft non-maximum suppression was proposed in this paper. To validate the model’s feasibility, the improved YOLOv5s model was first pre-trained using the sturgeon dataset, followed by further training on the occlusion dataset for segmentation tasks. Based on the phenotypic data obtained from the improved model, a multilayer perceptron method was used to estimate the sturgeon’s weight accurately. Experimental results demonstrated that the average precision of the improved YOLOv5s model reached 89.80% under occlusion conditions, and the correlation coefficient of noncontact weight measurement results reached 89.80%. The experimental results showed that the improved algorithm effectively performs segmentation of sturgeon in occlusion conditions and can accurately estimate the mass.

Tightly Coupled LIDAR/IMU/UWB Fusion via Resilient Factor Graph for Quadruped Robot Positioning

Continuous accurate positioning in global navigation satellite system (GNSS)-denied environments is essential for robot navigation. Significant advances have been made with light detection and ranging (LiDAR)-inertial measurement unit (IMU) techniques, especially in challenging environments with varying lighting and other complexities. However, the LiDAR/IMU method relies on a recursive positioning principle, resulting in the gradual accumulation and dispersion of errors over time. To address these challenges, this study proposes a tightly coupled LiDAR/IMU/UWB fusion approach that integrates an ultra-wideband (UWB) positioning technique. First, a lightweight point cloud segmentation and constraint algorithm is designed to minimize elevation errors and reduce computational demands. Second, a multi-decision non-line-of-sight (NLOS) recognition module using information entropy is employed to mitigate NLOS errors. Finally, a tightly coupled framework via a resilient mechanism is proposed to achieve reliable position estimation for quadruped robots. Experimental results demonstrate that our system provides robust positioning results even in LiDAR-limited and NLOS conditions, maintaining low time costs.

An algorithm for lane detection based on RIME optimization and optimal threshold

In order to address the challenges of low lane line detection rates caused by complex road conditions,we propose a novel algorithm that integrates frost and ice optimisation with optimal thresholding. A pre-processing model based on Retinex theory is used to reduce noise and preserve grey scale detail. The optimal OTSU threshold is determined for segmentation, which is enhanced by tent mapping. To further enhance the precision of the detection process, the binarized image is transformed into a bird’s-eye view, and the lane line pixel features are identified through the use of an adaptive sliding window. Ultimately, the RANSAC algorithm is utilized in conjunction with a parabolic model for lane line fitting. The experimental results demonstrate that, in comparison to similar image segmentation algorithms, the proposed method exhibits a notable advantage in terms of threshold calculation error and computational efficiency. Moreover, in comparison to analogous line detection algorithms, the detection accuracy rate reaches 93.87%, effectively reducing the impact of interference factors and demonstrating remarkable robustness that surpasses the traditional Hough Transform, which has an accuracy of 43.2%, and sliding window and Hough transform, with an accuracy of 89.16%. The code of our research work is publicly available at: https://github.com/zx2000430/rime.