Search

Pedestrian trajectory prediction is one of the most important topics to be researched for unmanned driving and intelligent mobile robots to perform perceptual interaction with the environment. To solve the problem of the SGAN (social generative adversarial networks) model lacking an understanding of pedestrian interaction and scene constraints, this paper proposes a trajectory prediction method based on a scenario-constrained generative adversarial network. Firstly, a self-attention mechanism is added, which can integrate information at every moment. Secondly, mutual information is introduced to enhance the influence of latent code on the predicted trajectory. Finally, a new social pool is introduced into the original trajectory prediction model, and a scene edge extraction module is added to ensure the final output path of the model is within the passable area in line with the physical scene, which greatly improves the accuracy of trajectory prediction. Based on the CARLA (CAR Learning to Act) simulation platform, the improved model was tested on the public dataset and the self-built dataset. The experimental results showed that the average moving deviation was reduced by 26.4 % and the final offset was reduced by 23.8 % , which proved that the improved model could better solve the uncertainty of pedestrian turning decisions. The accuracy and stability of pedestrian trajectory prediction are improved while maintaining multiple modes. [ABSTRACT FROM AUTHOR]

The wear condition of steel rails directly affects the safety of railway operations. Line-structured-light visual measurement technology is used for online measurement of rail wear due to its ability to achieve high-precision dynamic measurements. However, in dynamic measurements, the random deviation of the measurement plane caused by the vibration of the railcar results in changes in the actual measured rail profile relative to its cross-sectional profile, ultimately leading to measurement deviations. To address these issues, this paper proposes a method for three-dimensional measurement of steel rail cross-sectional profiles based on binocular line-structured light. Firstly, calibrated dual cameras are used to simultaneously capture the profiles of both sides of the steel rail in the same world coordinate system, forming the complete rail profile. Then, considering that the wear at the rail waist is zero in actual operation, the coordinate of the circle center on both sides of the rail waist are connected to form feature vectors. The measured steel rail profile is aligned with the corresponding feature vectors of the standard steel rail model to achieve initial registration; next, the rail profile that has completed the preliminary matching is accurately matched with the target model based on the iterative closest point (ICP) algorithm. Finally, by comparing the projected complete rail profile onto the rail cross-sectional plane with the standard 3D rail model, the amount of wear on the railhead can be obtained. The experimental results indicate that the proposed line-structured-light measurement method for the complete rail profile, when compared to the measurements obtained from the rail wear gauge, exhibits smaller mean absolute deviation (MAD) and root mean square error (RMSE) for both the vertical and lateral dimensions. The MAD values for the vertical and lateral measurements are 0.009 mm and 0.039 mm, respectively, while the RMSE values are 0.011 mm and 0.048 mm. The MAD and RMSE values for the vertical and lateral wear measurements are lower than those obtained using the standard two-dimensional rail profile measurement method. Furthermore, it effectively eliminates the impact of vibrations during the dynamic measurement process, showcasing its practical engineering application value. [ABSTRACT FROM AUTHOR]

Visual-based object detection systems are essential components of intelligent equipment for water surface environments. The diversity of water surface target types, uneven distribution of sizes, and difficulties in dataset construction pose significant challenges for water surface object detection. This article proposes an improved YOLOv5 target detection method to address the characteristics of diverse types, large quantities, and multiple scales of actual water surface targets. The improved YOLOv5 model optimizes the extraction of bounding boxes using K-means++ to obtain a broader distribution of predefined bounding boxes, thereby enhancing the detection accuracy for multi-scale targets. We introduce the GAMAttention mechanism into the backbone network of the model to alleviate the significant performance difference between large and small targets caused by their multi-scale nature. The spatial pyramid pooling module in the backbone network is replaced to enhance the perception ability of the model in segmenting targets of different scales. Finally, the Focal loss classification loss function is incorporated to address the issues of overfitting and poor accuracy caused by imbalanced class distribution in the training data. We conduct comparative tests on a self-constructed dataset comprising ten categories of water surface targets using four algorithms: Faster R-CNN, YOLOv4, YOLOv5, and the proposed improved YOLOv5. The experimental results demonstrate that the improved model achieves the best detection accuracy, with an 8% improvement in mAP@0.5 compared to the original YOLOv5 in multi-scale water surface object detection. [ABSTRACT FROM AUTHOR]

Aiming at the problem of detection difficulties caused by the characteristics of high similarity and disorderly arrangement of workpieces in dense scenes of industrial production lines, this paper proposes a workpiece detection method based on improved YOLOv5, which embeds a coordinate attention mechanism in the feature extraction network to enhance the network's focus on important features and enhance the model's ability to pinpoint targets. The pooling structure of the space pyramid has been replaced, which reduces the amount of calculation and further improves the running speed. A weighted bidirectional feature pyramid is introduced in the feature fusion network to realize efficient bidirectional cross-scale connection and weighted feature fusion, and improve the detection ability of small targets and dense targets. The SIoU loss function is used to improve the training speed and further improve the detection performance of the model. The average accuracy of the improved model on the self-built artifact dataset is improved by 5% compared with the original model and the number of model parameters is 14.6MB, which is only 0.5MB higher than the original model. It is proved that the improved model has the characteristics of high detection accuracy, strong robustness and light weight. [ABSTRACT FROM AUTHOR]

A high-definition map of the autonomous driving system was built with the target points of interest, which were extracted from a large amount of unordered raw point cloud data obtained by Lidar. In order to better obtain the target points of interest, this paper proposes an improved RandLa-Net algorithm incorporated with NDT registration, which can be used to automatically classify and extract large-scale raw point clouds. First, based on the NDT registration algorithm, the frame-by-frame raw point cloud data were converted into a point cloud global map; then, the RandLa-Net network combined random sampling with a local feature sampler is used to classify discrete points in the point cloud map point by point. Finally, the corresponding point cloud data were extracted for the labels of interest through numpy indexing. Experiments on public datasets senmatic3D and senmatickitti show that the method has excellent accuracy and processing speed for the classification and extraction of large-scale point cloud data acquired by Lidar. [ABSTRACT FROM AUTHOR]

A vaginal yolk sac tumor (YST) is a rare malignant germ cell tumor for infants and children, and it has been >50 years since the first case was reported. The treatment strategy has changed markedly in the past 50 years, from radical surgical treatment to conservative surgery combined with chemotherapy, and then to combined chemotherapy alone. The present study reports the case of a primary vaginal YST in a 13-month-old girl that was successfully treated by tumor resection combined with chemotherapy. The clinical symptoms, imaging features and treatment characteristics are described in detail, as well as the postoperative treatment. There was no local recurrence or metastasis for the 2 years of follow-up to date. A literature review was also conducted to investigate the clinicopathological features, treatment and prognosis of this tumor. Overall, surgery combined with bleomycin, etoposide and carboplatin combination chemotherapy can be an effective option for vaginal YST. [ABSTRACT FROM AUTHOR]

In order to improve face recognition accuracy, we present a simple near-infrared (NIR) and visible light (VL) image fusion algorithm based on two-dimensional linear discriminant analysis (2DLDA). We first use two such schemes to extract two classes of face discriminant features of each of NIR and VL images separately. Then the two classes of features of each kind of images are fused using the matching score fusion method. At last, a simple NIR and VL image fusion approach is exploited to combine the scores of NIR and VL images and to obtain the classification result. The experimental results show that the proposed NIR and VL image fusion approach can effectively improve the accuracy of face recognition. [ABSTRACT FROM PUBLISHER]

Although many epidemiologic studies have investigated the FXIII-A Val34Leu polymorphism and their associations with myocardial infarction (MI), definite conclusions can't be drawn. To clarify the effects of FXIII-A Val34Leu polymorphism on the risk of MI, a meta-analysis was performed. Related studies were identified from PubMed, Springer Link, Ovid, Chinese Wanfang Data Knowledge Service Platform, Chinese National Knowledge Infrastructure (CNKI), and Chinese Biology Medicine (CBM) till 10 August 2014. Pooled ORs and 95% CIs were used to assess the strength of the associations. A total of 12 studies including 3139 MI cases and 6343 healthy controls were involved in this meta-analysis. A significantly decreased MI risk was found (adjusted OR = 0.70, 95% CI 0.60-0.82, P < 0.00001). In the subgroup analysis by age, significantly decreased risks were found in the young population (OR = 0.70, 95% CI 0.54-0.91, P = 0.008) and old population (OR = 0.63, 95% CI 0.50-0.80, P = 0.0001). In the subgroup analysis by gender, significantly decreased risks were found in male (OR = 0.55, 95% CI 0.34-0.88, P = 0.01) and female (OR = 0.72, 95% CI 0.55-0.95, P = 0.02). When we limited the meta-analysis to studies that controlled for confounders such as age, sex, BMI, smoking, diabetes, hypertension, dyslipidemia, and fibrinogen, a significant association between FXIII-A Val34Leu polymorphism and MI risk remained. This meta-analysis provides the evidence that FXIII-A Val34Leu polymorphism may significant associated with the MI risk.