Image Coordinate System Research Articles

Vibration displacement measurement of bridge structural models using image super-resolution reconstruction and visual object detection network

Abstract Visual vibration measurement has emerged in the field of structural health monitoring in recent years, but it still has some shortcomings in terms of resolution, recognition rate and real-time performance. Considering the three aspects of recovering high-frequency image details, improving the compactness of the target bounding box, and reducing the computational time, we use the constructed image super-resolution reconstruction model and target detection model to measure the vibration displacement of the bridge structural model. First, we integrate the Transformer module into the Unet network with a simple structure. The Swin and Global Transformer Unet (SGTU) module constructed in this form can reduce the computational cost while reconstructing the large-resolution feature map target, and it can sharply edge information of the vibration target. We use the framework of the YOLOv5 algorithm as the backbone, and use the GhostBottleneck (GB) module to reduce the time for convolution operations to generate similar features. In addition, the proposed DWCBottleneck (DWCB) fusion module is also able to achieve high-level semantic fusion and network depth expansion with minimal computational cost. Finally, the center point offset of the bounding box predicted by the model can be used to obtain the displacement offset of the object in the image sequence. The position information of the target in the first frame image is used as the reference frame for calculating the offset, and the vibration displacement of the flexible structure in the image coordinate system is obtained by calculating the deviation of the displacement between the remaining frames and the first frame. We perform qualitative and quantitative comparisons in three aspects: video super-resolution reconstruction, visual detection robustness, and sensor vibration measurement displacement using a homemade vibration image dataset. The time-frequency domain displacement curves regressed by the visual vibration measurement algorithm are compared with the curves acquired after accelerometer acquisition, indicating the necessity of super-resolution reconstruction in visual vibration measurement.

A new method with C-means segmentation for non-uniform image coordinate system definition in panoramic imaging employing Ladybug2 camera

A panorama ensures a stunning wide-angle field of view up to 360° representation of a scene, exceeding the limits of a normal photograph. Panoramic cameras satisfy the single-viewpoint characteristic. There are several types of panoramic cameras for 360-degree imaging. Multi-camera panoramic imaging systems pose a difficulty in obtaining a single projection center for the cameras. In a variety of practical implementations of panoramic cameras, it is possible to calculate three-dimensional coordinates from a panoramic image, especially using the Direct Linear Transformation (DLT) method. In this study, not only a defining method of the non-uniform image coordinate system is presented by utilizing the C-Means algorithm for a single panoramic image, captured with a Ladybug2 panoramic camera in a panoramic calibration room but also the use of an elliptical panoramic projection coordinate system is defined by the Singular Value Decomposition method in a panoramic view. The results of the suggested method have been compared with the DLT algorithm for a single panoramic image which defined a conventional photogrammetric image coordinate system. It has been observed that the proposed method provides more accurate results for the 3D coordinate definition.

An Advanced IBVS-Flatness Approach for Real-Time Quadrotor Navigation: A Full Control Scheme in the Image Plane

This article presents an innovative method for planning and tracking the trajectory in the image plane for the visual control of a quadrotor. The community of researchers working on 2D control widely recognizes this challenge as complex, because a trajectory defined in image space can lead to unpredictable movements of the robot in Cartesian space. While researchers have addressed this problem for mobile robots, quadrotors continue to face significant challenges. To tackle this issue, the adopted approach involves considering the separation of altitude control from the other variables, thus reducing the workspace. Furthermore, the movements of the quadrotor (pitch, roll, and yaw) are interdependent. Consequently, the connection between the inputs and outputs cannot be reversed. The task complexity becomes significant. To address this issue, we propose the following scenario: When the quadrotor is equipped with a downward-facing camera, flying at high altitude is sensible to spot a target. However, to minimize disturbances and conserve energy, the quadrotor needs to descend in altitude. This can result in the target being lost. The solution to this problem is a new methodology based on the principle of differential flatness, allowing the separation of altitude control from the other variables. The system first detects the target at high altitude, then plots a trajectory in the image coordinate system between the acquired image and the desired image. It is crucial to emphasize that this step is performed offline, ensuring that the image processing time does not affect the control frequency. Through the proposed trajectory planning, complying with the constraints of differential flatness, the quadrotor can follow the imposed dynamics. To ensure the tracking of the target while following the generated trajectory, the proposed control law takes the form of an Image Based Visual Servoing (IBVS) scheme. We validated this method using the RVCTOOLS environment in MATLAB. The DJI Phantom 1 quadrotor served as a testbed to evaluate, under real conditions, the effectiveness of the proposed control law. We specifically designed an electronic card to transfer calculated commands to the DJI Phantom 1 control joystick via Bluetooth. This card integrates a PIC18F2520 microcontroller, a DAC8564 digital-to-analogue converter, and an RN42 Bluetooth module. The experimental results demonstrate the effectiveness of this method, ensuring the precise tracking of the target as well as the accurate tracking of the path generated in the image coordinate system.

Innovative K-Means based machine learning method for determination of non-uniform image coordinate system in panoramic imaging: a case study with Ladybug2 camera.

Currently, the practical implementations of panoramic cameras range from vehicle navigation to space studies due to their 360-degree imaging capability in particular. In this variety of uses, it is possible to calculate three-dimensional coordinates from a panoramic image, especially using the Direct Linear Transformation (DLT) method. There are several types of omnidirectional cameras which can be classified mainly as central and non-central cameras for 360-degree imaging. The central omnidirectional cameras are those which satisfy the single-viewpoint characteristic. Multi-camera systems are usually developed for applications for which two-image stereo vision is not flexible enough to capture the environment surrounding a moving platform. Although the technology based on multi-view geometry is inexpensive, accessible, and highly customizable, multi-camera panoramic imaging systems pose a difficulty in obtaining a single projection center for the cameras. In this study, not only a defining method of the non-uniform image coordinate system is suggested by means of the K-Means algorithm for a single panoramic image, captured with a Ladybug2 panoramic camera in the panoramic calibration room but also the use of an elliptical panoramic projection coordinate system definition by Singular Value Decomposition (SVD) method in panoramic view. The results of the suggested method have been compared with the DLT algorithm for a single panoramic image which defined a conventional photogrammetric image coordinate system.

Research on the Innovation of Education Mode of Art and Design Specialties in Colleges and Universities under the Background of Big Data

Abstract Art and design professional education can not keep up with the development speed of the industry, and there is no reference to the market demand data, failing to keep pace with the times, which brings many negative impacts on the employment of students. This paper suggests an art design professional education model that incorporates VR interactive scene technology for this reason. Through the double camera, we simulate the visual principle of the human eye, collect three-dimensional data from VR interactive scenes, and use the image coordinate system and pixel coordinate system conversion operations to pre-process the collected data. The optimal layout solution is sought by adjusting the layout to generate a realistic 3D model of the teaching scene and to realize the construction of the VR interactive scene. The principle and implementation process of the art professional education model supported by the VR interactive scene is elaborated, and the experimental comparison method is used to empirically analyze the art and design professional education model integrating VR technology. The results show that there is a significant difference between the experimental group and the control group in the dimensions of adaptability (0.004) and uniqueness (0.044) of design thinking and the cultivation of design thinking ability (0.016) (P<0.05), which indicates that the art and design professional education model integrating the VR scene interaction technology constructed in this paper has a significant effect on the enhancement of design thinking ability of college students.

4D hyperspectral surface topography measurement system based on the Scheimpflug principle and hyperspectral imaging.

A four-dimensional (4D) hyperspectral surface topography measurement (HSTM) system that can acquire uniform inelastic signals [three-dimensional (3D) spatial data] and reflection/fluorescence spectra of an object is proposed. The key components of the system are a light-sheet profilometer based on the Scheimpflug principle and a hyperspectral imager. Based on the mapping relationships among the image coordinate systems of the two imaging subsystems and the coordinate system of the real space, the spectral data can be assigned to the corresponding 3D point cloud, forming a 4D model. The spectral resolution is better than 4nm. 700nm, 546nm, and 436nm are selected as the three primary colors of red, green, and blue to restore the color. The 4D hyperspectral surface reconstruction experiments of philodendron and chlorophytum have shown the good performance of the proposed HSTM system and the great application potential for plant phenotype and growth analysis in agriculture.

Introduction and preliminary application report for a novel 3D printed perforator navigator for fibular flap surgery

The aim of this study was to introduce and report on a 3D-printed perforator navigator and its clinical application.Integrated imaging and 3D printing techniques were employed for the design and manufacture of a perforator navigator. Key techniques included establishing a digital image coordinate system, localizing perforator fascia piercing points, creating a reference plane for the perforator course, and projecting the perforator course onto the body surface.All cases of maxillofacial defect repaired with free fibular myocutaneous flaps, from January 2019 to January 2022, were reinvestigated. Patients treated using traditional perforator localization methods were assigned into group Ⅰ, while those who had a navigator used during treatment were allocated to group Ⅱ. Outcome measurements included perforator positioning accuracy, perforator preparation time (PT), and flap growth score. Capillary refilling time and degree of flap swelling were recorded on the 1st, 3rd, and 7th days after surgery. On the 10th day after surgery, the flap survival situation was graded.In total, 25 patients were included in the study. Perforator preparation time for group Ⅱ was significantly less (p ＝ 0.04) than for group Ⅰ (1038.6 ± 195.4 s versus 1271.4 ± 295.1 s. In group Ⅱ, the mean positioning deviation for the perforator navigator was 2.12 cm less than that for the high-frequency color Doppler (p ＝ 0.001). Group Ⅱ also had a higher score than group Ⅰ for overall flap growth evaluation (nonparametric rank sum test, p ＝ 0.04).Within the scale of the study, it seems that perforator localization and navigation using a 3D-printed navigator is technically feasible, and helps to improve the clinical outcome of free fibular flaps. The perforator navigator will play a useful role in displaying the perforator course, improving the accuracy of perforator localization, reducing surgical injury, and ultimately enhancing flap success rate.

基于PID-模糊控制的单目视觉农田机器人视觉导航系统设计

This study proposes a monocular vision navigation control system based on PID-fuzzy control, which travels along the edge of the path. It collects path image information through monocular vision, identifies the path edge through image processing to determine the preview point, and uses a combination of PID and fuzzy control to design a controller to track the preview point for path navigation. Firstly, coordinate calibration and conversion were performed on the monocular camera, achieving coordinate conversion from the image coordinate system to the world coordinate system. The accuracy of the calibration results was verified through experiments. According to the navigation strategy of driving along the edge of the path, the world coordinate equation of the path edge is obtained through image processing technology, and the preview point tracked by the navigation system is determined. The navigation parameters are determined based on the position of the preview point. The PID fuzzy controller system designed in this study can switch different control methods based on the position of the preview point. Finally, an experimental verification was conducted on the monocular visual navigation system of the control system. The verification results showed that the average error of the navigation control system in tracking the path when driving in a straight line was 0.039 m, the average error when turning left was 0.079 m, and the average error when turning right was 0.121 m. The error range can meet the basic requirements of agricultural robot farmland operations. Research has shown that the navigation strategy based on PID-fuzzy joint controller to track the preview point along the path edge has a good effect on the visual navigation control system of agricultural robots. This study provides important reference value for the research and development of monocular visual navigation systems of agricultural robots.

Image data harmonization tools for the analysis of post-traumatic epilepsy development in preclinical multisite MRI studies

Preclinical MRI studies have been utilized for the discovery of biomarkers that predict post-traumatic epilepsy (PTE). However, these single site studies often lack statistical power due to limited and homogeneous datasets. Therefore, multisite studies, such as the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), are developed to create large, heterogeneous datasets that can lead to more statistically significant results. EpiBioS4Rx collects preclinical data internationally across sites, including the United States, Finland, and Australia. However, in doing so, there are robust normalization and harmonization processes that are required to obtain statistically significant and generalizable results. This work describes the tools and procedures used to harmonize multisite, multimodal preclinical imaging data acquired by EpiBioS4Rx. There were four main harmonization processes that were utilized, including file format harmonization, naming convention harmonization, image coordinate system harmonization, and diffusion tensor imaging (DTI) metrics harmonization. By using Python tools and bash scripts, the file formats, file names, and image coordinate systems are harmonized across all the sites. To harmonize DTI metrics, values are estimated for each voxel in an image to generate a histogram representing the whole image. Then, the Quantitative Imaging Toolkit (QIT) modules are utilized to scale the mode to a value of one and depict the subsequent harmonized histogram. The standardization of file formats, naming conventions, coordinate systems, and DTI metrics are qualitatively assessed. The histograms of the DTI metrics were generated for all the individual rodents per site. For inter-site analysis, an average of the individual scans was calculated to create a histogram that represents each site. In order to ensure the analysis can be run at the level of individual animals, the sham and TBI cohort were analyzed separately, which depicted the same harmonization factor. The results demonstrate that these processes qualitatively standardize the file formats, naming conventions, coordinate systems, and DTI metrics of the data. This assists in the ability to share data across the study, as well as disseminate tools that can help other researchers to strengthen the statistical power of their studies and analyze data more cohesively.

Three-dimensional measurement for specular reflection object based on adaptive fringe projection and phase measuring profilometry

ObjectiveUsing phase measuring profilometry (PMP) to measure the object with specular reflections is a very challenging task, a large deviation between the measured result and the actual data is caused by the highlights of the measured object surface. Therefore, a 3D measurement method for the object with a specular reflection based on adaptive fringe pattern projection (AFPP) is proposed in this paper. MethodsFirstly, a set of uniform grayscale images are projected to the measured object by a DLP projector. The gray level gradually decreases from 255 and stops when the number of saturated pixels in the camera image reaches 0. The masks of saturated pixels at each gray level are obtained. Secondly, the masks corresponding to two adjacent gray levels are compared respectively, and the optimal projection intensity of the camera image is obtained. Thirdly, the coordinates of the highlight pixels in the camera image are mapped to the projector image coordinate system, and the new fringe images are generated using the optimal projection intensity. Fourthly, the new fringe images are projected onto the measured object for 3D measurement. ResultsThe measurement experiments were implemented on the measured objects made of metal, plastic, and ceramic, respectively. Experiment results show that the proposed method can reduce the measurement deviation caused by the highlights and make the 3D reconstruction model more complete without additional cameras and hardware.

Recognizing Trained and Untrained Obstacles around a Port Transfer Crane Using an Image Segmentation Model and Coordinate Mapping between the Ground and Image.

Container yard congestion can become a bottleneck in port logistics and result in accidents. Therefore, transfer cranes, which were previously operated manually, are being automated to increase their work efficiency. Moreover, LiDAR is used for recognizing obstacles. However, LiDAR cannot distinguish obstacle types; thus, cranes must move slowly in the risk area, regardless of the obstacle, which reduces their work efficiency. In this study, a novel method for recognizing the position and class of trained and untrained obstacles around a crane using cameras installed on the crane was proposed. First, a semantic segmentation model, which was trained on images of obstacles and the ground, recognizes the obstacles in the camera images. Then, an image filter extracts the obstacle boundaries from the segmented image. Finally, the coordinate mapping table converts the obstacle boundaries in the image coordinate system to the real-world coordinate system. Estimating the distance of a truck with our method resulted in 32 cm error at a distance of 5 m and in 125 cm error at a distance of 30 m. The error of the proposed method is large compared with that of LiDAR; however, it is acceptable because vehicles in ports move at low speeds, and the error decreases as obstacles move closer.

Size and Topology Optimization of Truss Structures Using Quantum-Based HS Algorithm

With the development of various metaheuristic algorithms, research cases that perform weight optimization of truss structures are steadily progressing. In particular, due to the possibility of developing quantum computers, metaheuristic algorithms combined with quantum computation are being developed. In this paper, the QbHS (Quantum based Harmony Search) algorithm was proposed by combining quantum computation and the conventional HS (Harmony Search) algorithms, and the size and topology optimization of the truss structure was performed. The QbHS algorithm has the same repetitive computational structure as the conventional HS algorithm. However, the QbHS algorithm constructed QHM (Quantum Harmony Memory) using the probability of Q-bit and proposed to perform pitch adjusting using the basic state of Q-bit. To perform weight optimization of truss structures using the proposed QbHS algorithm, 20 bar, 24 bar, and 72-bar truss structures were adopted as examples and compared with the results of the QE (Quantum Evolutionary) algorithm. As a result, it was confirmed that the QbHS algorithm had excellent convergence performance by finding a lower weight than the QE algorithm. In addition, by expressing the weight optimization results of the truss structure with an image coordinate system, the topology of the truss structure could be confirmed only by the picture. The results of this study are expected to play an important role in future computer information systems by combining quantum computation and conventional HS algorithms.

Hand-Eye Calibration via Linear and Nonlinear Regressions

For a robot to pick up an object viewed by a camera, the object’s position in the image coordinate system must be converted to the robot coordinate system. Recently, a neural network-based method was proposed to achieve this task. This methodology can accurately convert the object’s position despite errors and disturbances that arise in a real-world environment, such as the deflection of a robot arm triggered by changes in the robot’s posture. However, this method has some drawbacks, such as the need for significant effort in model selection, hyperparameter tuning, and lack of stability and interpretability in the learning results. To address these issues, a method involving linear and nonlinear regressions is proposed. First, linear regression is employed to convert the object’s position from the image coordinate system to the robot base coordinate system. Next, B-splines-based nonlinear regression is applied to address the errors and disturbances that occur in a real-world environment. Since this approach is more stable and has better calibration performance with interpretability as opposed to the recent method, it is more practical. In the experiment, calibration results were incorporated into a robot, and its performance was evaluated quantitatively. The proposed method achieved a mean position error of 0.5 mm, while the neural network-based method achieved an error of 1.1 mm.

Localization on a-priori information of plane extraction.

Localization constitutes a critical challenge for autonomous mobile robots, with flattened walls serving as a fundamental reference for indoor localization. In numerous scenarios, prior knowledge of a wall's surface plane is available, such as planes in building information modeling (BIM) systems. This article presents a localization technique based on a-priori plane point cloud extraction. The position and pose of the mobile robot are estimated through real-time multi-plane constraints. An extended image coordinate system is proposed to represent any planes in space and establish correspondences between visible planes and those in the world coordinate system. Potentially visible points representing the constrained plane in the real-time point cloud are filtered using the filter region of interest (ROI), derived from the theoretical visible plane region within the extended image coordinate system. The number of points representing the plane influences the calculation weight in the multi-plane localization approach. Experimental validation of the proposed localization method demonstrates its allowance for redundancy in initial position and pose error.

A high-accuracy positioning method for mobile robotic grasping with monocular vision and long-distance deviation

It is difficult for mobile robot to position and grasp large-sized objects accurately because the vision acquisition camera deviates from the rotation axis of the robot with a long distance, which leads to the magnification of the calibration error. A method of identifying target objects by positioning marked targets with monocular vision was developed. The marked target was placed in a fixed position within the field of view, which was no longer limited by the specific size of the target object. And the corresponding relation between the displacement of the robot relative coordinate system and the image coordinate system was established. Based on the surface fitting method of Gaussian process regression (GPR), the error compensation model was constructed to effectively compensate and correct the operating errors of the robot once again, so that the accuracy of the error compensation model can meet the goal of accurate positioning and grasping for large-sized objects. Specifically, the error data of each positioning was collected by comparing the real value of the robot's positioning with the estimated value of vision, and the error distribution model of the whole field of vision can be obtained. More than 1000 times of printed circuit board (PCB) board accurate loading and unloading tests were carried out with PCB board moving rack and mobile robot operating platform. The results showed that it could achieve precise visual positioning, which met the industrial application requirements for the mobile robot operating platform.

Weld Cross-Section Profile Fitting and Geometric Dimension Measurement Method Based on Machine Vision

A visual measurement method based on a key point detection network is proposed for the difficulty of fitting the cross-sectional profile of ultra-narrow gap welds and the low efficiency and accuracy of manual measurement of geometric parameters. First, the HRnet (High-Resolution Net) key point detection algorithm was used to train the feature point detection model, and 18 profile feature points in a “measurement unit” were extracted. Secondly, the feature point coordinates are transformed from the image coordinate system to the weld coordinate system, and the weld profiles are fitted by the least squares method. Finally, the measurement system is calibrated with a coplanar linear calibration algorithm to perform pixel distance to actual distance conversion for quantitative detection of geometric dimensions. The experimental results show that the accuracy of the proposed method for key point localization is 95.6%, the mean value of the coefficient of determination R-square for curve fitting is greater than 94%, the absolute error of measurement is between 0.06 and 0.15 mm, and the relative error is between 1.27% and 3.12%. The measurement results are more reliable, and the efficiency is significantly improved compared to manual measurement.

Improving maritime traffic surveillance in inland waterways using the robust fusion of AIS and visual data

To guarantee vessel traffic safety in inland waterways, the automatic identification system (AIS) and shore-based cameras have been widely adopted to monitor moving vessels. The AIS data could provide the unique maritime mobile service identity (MMSI), position coordinates (i.e., latitude and longitude), course over ground, and speed over ground for the vessels of interest. In contrast, the cameras could directly display the visual appearance of vessels but fail to accurately grasp the vessels’ identity information and motion parameters. In this paper, we propose to improve the maritime traffic surveillance in inland waterways using the robust fusion of AIS and visual data. It is able to obtain more accurate vessel tracking results and kinematic characteristics. In particular, to robustly track the visual vessels under complex scenarios, we first propose an anti-occlusion vessel tracking method based on the simple online and real-time tracking with a deep association metric (DeepSORT) method. We then preprocess and predict the vessel positions to obtain synchronous AIS and visual data. Before the implementation of AIS and visual data fusion, the AIS position coordinates in the geodetic coordinate system will be projected into the image coordinate system via the coordinate transformation. A multi-feature similarity measurement-based Hungarian algorithm is finally proposed to robustly and accurately fuse the AIS and visual data in the image coordinate system. For the sake of repeating fusion experiments, we have also presented a new multi-sensor dataset containing AIS data and shore-based camera imagery. The quantitative and qualitative experiments show that our fusion method is capable of improving the maritime traffic surveillance in inland waterways. It can overcome the vessel occlusion problem and fully utilizes the advantages of multi-source data to promote the maritime surveillance, resulting in enhanced vessel traffic safety and efficiency. In this work, the presented multi-sensor dataset and source code are available at https://github.com/QuJX/AIS-Visual-Fusion.

Improved calibration method for displacement transformation coefficient in optical and visual measurements

Optical and visual measurement technology is used widely in fields that involve geometric measurements, and among such technology are laser and vision-based displacement measuring modules (LVDMMs). The displacement transformation coefficient (DTC) of an LVDMM changes with the coordinates in the camera image coordinate system during the displacement measuring process, and these changes affect the displacement measurement accuracy of LVDMMs in the full field of view (FFOV). To give LVDMMs higher accuracy in the FFOV and make them adaptable to widely varying measurement demands, a new calibration method is proposed to improve the displacement measurement accuracy of LVDMMs in the FFOV. First, an image coordinate system, a pixel measurement coordinate system, and a displacement measurement coordinate system are established on the laser receiving screen of the LVDMM. In addition, marker spots in the FFOV are selected, and the DTCs at the marker spots are obtained from calibration experiments. Also, a fitting method based on locally weighted scatterplot smoothing (LOWESS) is selected, and with this fitting method the distribution functions of the DTCs in the FFOV are obtained based on the DTCs at the marker spots. Finally, the calibrated distribution functions of the DTCs are applied to the LVDMM, and experiments conducted to verify the displacement measurement accuracies are reported. The results show that the FFOV measurement accuracies for horizontal and vertical displacements are better than ±15 µm and ±19 µm, respectively, and that for oblique displacement is better than ±24 µm. Compared with the traditional calibration method, the displacement measurement error in the FFOV is now 90% smaller. This research on an improved calibration method has certain significance for improving the measurement accuracy of LVDMMs in the FFOV, and it provides a new method and idea for other vision-based fields in which camera parameters must be calibrated.

A Lightweight LiDAR-Camera Sensing Method of Obstacles Detection and Classification for Autonomous Rail Rapid Transit

Detecting obstacles accurately is a vital part of realizing autonomous driving. But utilizing single sensors, for example, RGB camera or LiDAR itself, is difficult to meet the needs of autonomous driving under the condition of sparse environmental sensing data. In contrast, equipments that can collect data in large quantities are expensive and difficult to be utilized in practical applications. Hence, under the premise of ensuring low application costs, the difficult task is to obtain high-precision three-dimensional information of obstacles in real-time from sparse data. To overcome this problem, a real-time detection technique that combines LiDAR data and image information for obstacles detection is proposed in this article. It can detect vehicles close to the ART in sparse data in real-time. The sparse point cloud is de-noised initially and the ground is separated from the initial cloud data. The remained cloud, which includes surrounding vehicles, are processed by the clustering algorithm according to the structural relationship and scanning mechanism of the LiDAR. Then the processed LiDAR data is fused with the classification information generated from images based on a neural network to locate suspicious obstacles. The ratio values between the central values of vehicles and image width are obtained in the image coordinate system. Then the category information and ratio values are mapped on the processed LiDAR clusters during ART operation. The method is tested and verified in the real field. The evaluation results show that the proposed method can achieve an average accuracy of 85.5% for obstacle classification with a cost-effective sensing suit.

ProTheRaMon—a GATE simulation framework for proton therapy range monitoring using PET imaging

Objective. This paper reports on the implementation and shows examples of the use of the ProTheRaMon framework for simulating the delivery of proton therapy treatment plans and range monitoring using positron emission tomography (PET). ProTheRaMon offers complete processing of proton therapy treatment plans, patient CT geometries, and intra-treatment PET imaging, taking into account therapy and imaging coordinate systems and activity decay during the PET imaging protocol specific to a given proton therapy facility. We present the ProTheRaMon framework and illustrate its potential use case and data processing steps for a patient treated at the Cyclotron Centre Bronowice (CCB) proton therapy center in Krakow, Poland. Approach. The ProTheRaMon framework is based on GATE Monte Carlo software, the CASToR reconstruction package and in-house developed Python and bash scripts. The framework consists of five separated simulation and data processing steps, that can be further optimized according to the user’s needs and specific settings of a given proton therapy facility and PET scanner design. Main results. ProTheRaMon is presented using example data from a patient treated at CCB and the J-PET scanner to demonstrate the application of the framework for proton therapy range monitoring. The output of each simulation and data processing stage is described and visualized. Significance. We demonstrate that the ProTheRaMon simulation platform is a high-performance tool, capable of running on a computational cluster and suitable for multi-parameter studies, with databases consisting of large number of patients, as well as different PET scanner geometries and settings for range monitoring in a clinical environment. Due to its modular structure, the ProTheRaMon framework can be adjusted for different proton therapy centers and/or different PET detector geometries. It is available to the community via github (Borys et al 2022).