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Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem of inadequate perception of autonomous driving in occlusion and over-the-horizon scenarios, a vehicle-road collaborative perception method based on a dual-stream feature extraction network is proposed to enhance the 3D object detection capabilities of traffic participants. Feature extraction networks for roadside and vehicle-side scenes are tailored based on respective characteristics. Since roadside has rich and sufficient sensing data and computational resources, the Transformer structure is used to extract more sophisticated and advanced feature representations. Due to limited computational capability and high real-time demands of autonomous vehicles, partial convolution (PConv) is employed to enhance computing efficiency, and the Mamba-VSS module is introduced for efficient perception in complex environments. Collaborative perception between vehicle-side and roadside is accomplished through the selective sharing and fusion of critical perceptual information guided by confidence maps. By training and testing on DAIR-V2X dataset, the model size of vehicle-side feature extraction network is obtained to be 8.1 MB, and the IoU thresholds of 0.5 and 0.7 correspond to the average accuracy indexes of 67.67% and 53.74%. The experiment verifies the advantages of this method in detection accuracy and model size, and provides a lower-configuration detection scheme for vehicle-road collaboration.

In order to accurately identify the target obstacle in the traffic scene, considering the complexity of the real road scene and the importance of road safety, taking the sparsely embedded convolutional detection( SECOND) model as the basic model, the point cloud representation ability was enhanced by using the self-attention mechanism to obtain global semantic information, and the region of interest (RoI) detection head was used to optimize the 3D suggestion box generated by the candidate region to improve its detection accuracy. Then, two-stage 3D object detection method based on self-attention mechanism named SAR-SECOND was proposed. The results show that compared with the existing advanced 3D object detection methods, SAR-SECOND overall detection accuracy of car is 82. 28%, the overall accuracy of pedestrians is 51. 45%, and the overall accuracy of the rider is 72. 41% . It can be seen that the effectiveness of this method is effective. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low detection accuracy caused by insufficient target feature extraction in substation 3D scene, a voxelization-based 3D object detection model AugSecond for substation scene is proposed, which is designed based on the Second network structure. It introduces a triple attention mechanism in the voxel feature encoding stage, which focuses on multi-dimensional attention to enhance the key information of the target and reduce the interference of irrelevant feature information. It designes asymmetric sparse convolutional networks, uses asymmetric convolution to improve convolutional kernel representation capabilities and fuses multi-scale features to enrich target geometry information. Meanwhile, the position regression loss is optimized, and CIoU Loss is used to further consider the geometric correlation between bounding boxes to speed up the network convergence. Experiments on self-built power scene data sets and public data sets show that compared with the benchmark model, AugSecond model significantly improves recognition accuracy and has real-time reasoning speed, which proves the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a novel multimodal collaborative perception framework to enhance the situational awareness of autonomous vehicles. First, a multimodal fusion baseline system is built that effectively integrates Light Detection and Ranging (LiDAR) point clouds and camera images. This system provides a comparable benchmark for subsequent research. Second, various well-known feature fusion strategies are investigated in the context of collaborative scenarios, including channel-wise concatenation, element-wise summation, and transformer-based methods. This study aims to seamlessly integrate intermediate representations from different sensor modalities, facilitating an exhaustive assessment of their effects on model performance. Extensive experiments were conducted on a large-scale open-source simulation dataset, i.e., OPV2V. The results showed that attention-based multimodal fusion outperforms alternative solutions, delivering more precise target localization during complex traffic scenarios, thereby enhancing the safety and reliability of autonomous driving systems.

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automotive Digest is the property of Automotive Digest Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] With the rapid popularization of new energy vehicles and the vigorous development of autonomous driving technology, 3D object detection algorithms play a pivotal role in real road scenes. The LiDAR point clouds contain precise position and geometric structure information of the object, which can accurately describe the 3D space position of the target. Moreover, LiDAR makes environmental perception and route planning of unmanned vehicles a reality. However, cars in real scenes often fall into complex and difficult situations, such as occlusion and truncation of objects, which contribute to highly sparse clouds and incomplete contours. Therefore, the effective use of disordered and unevenly distributed point clouds for accurate 3D object detection has important research significance and practical value for the safety of autonomous driving. [Methods] This paper uses LiDAR point clouds in an autonomous driving scene to conduct in-depth research on a high-performance 3D object detection algorithm based on deep learning. The 3D object detection algorithm based on multibranch feature fusion (PSANet) is designed to improve the capacity and viability of autonomous driving technology. After the disordered point clouds are divided into regular voxels, the voxel feature coding module and convolutional neural network are used to learn the voxel features, and the sparse 3D data are compressed into a dense 2D bird's-eye view. Furthermore, the multiscale bird's-eye view features are deeply fused through the coarse and fine branches of the 2D backbone network. The splitting and aggregation feature pyramid module in the fine branch splits and aggregates the bird's-eye view features at different levels and realizes the deep fusion of semantic information, texture information, and context information of multiscale features to obtain more expressive features. The multiscale features in the coarse branch are fused after transposed convolution, and the precise original spatial location information is preserved. After the feature extraction of coarse and fine branches, the element-wise addition method can obtain more accurate and complete features for object classification, position regression, and orientation prediction. [Results] The experimental results on the KITTI dataset show that the average precision of PSANet in 3D object detection and bird's-eye view object detection tasks reach 81.72% and 88.25%, respectively. The inference speed on a single GTX 1080Ti GPU can reach 24 frames per second, and it shows strong robustness in complex scenes. Compared with the two-stage target detection algorithms MV3D, AVOD-FPN, F-PointNet, and IPOD, the average accuracy of 3D target detection of this algorithm increased by 18.21%, 5.89%, 8.94%, and 3.12%, respectively. Compared with the one-stage target detection algorithms VoxelNet, SECOND, PointPillars, and VoTr-SSD, the average accuracy of 3D target detection of this algorithm increased by 11.63%, 4.05%, 3.19%, 0.98%, 1.16%, and 0.7%, respectively. The detection speed of this algorithm improved by 14 frames per second compared with the two-stage algorithm PointRCNN with similar accuracy. [Conclusions] In comparison with other advanced algorithms, this algorithm exhibits strong performance, in which it can better balance the accuracy and speed of target detection in autonomous driving scenarios. Higher accuracy is required in the algorithm, whether one- or two-stage, for novel applications. The method with the highest efficiency must be used in an autonomous car. [ABSTRACT FROM AUTHOR]

To enhance the feature representation of region of interest (RoI), which incorporated a spatial context encoding module and soft regression loss, a grid self-attention mechanism 3D object detection method based on raw point cloud, named GT3D, was proposed.The spatial context encoding module was designed to effectively weight the local and spatial features of points through the attention mechanism, considering the contribution of different point cloud features for a more accurate feature representation.The soft regression loss was introduced to address label ambiguity arising during the data annotation phase.Experiments conducted on the public KITTI 3D object detection dataset demonstrate that the proposed method achieves significant improvements in detection accuracy compared to other publicly available point cloud-based 3D object detection methods.The detection results of the test set are submitted to the official KITTI server for public evaluation, achieving detection accuracies of 91.45%, 82.76%, and 79.74% for easy, moderate, and hard difficulty levels in car detection, respectively.

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

3D object detection is a fundamental problem in autonomous driving，virtual reality，robotics，and other applications.Its goal is to extract the most accurate 3D box characterizing interested targets from the disordered point clouds，such as the closest 3D box surrounding the pedestrians or vehicles.The target 3D box's location，size，and orientation are also output.Currently，there are two primary approaches for 3D object detection: (1) pure point cloud based 3D object detection，in which the point clouds are created by binocular vision，RGB-D camera，and lidar; (2) fusion-based 3D object detection based on the fusion of image and point cloud.The various representations of 3D point clouds were introduced.Then representative methods were introduced from three aspects: traditional machine learning techniques; non-fusion deep learning based algorithms; and multimodal fusion-based deep learning algorithms in progressive relation.The algorithms within and across each category were examined and compared，and the differences and connections between the various methods were analyzed thoroughly.Finally，remaining challenges of 3D object detection were discussed and explored.And the primary datasets and metrics used in 3D object detection studies were summarized.

3D object detection is a new research direction in recent years, and its main task is the location and recognization of targets in space. The existing methods for 3D object detection using monocular or binocular stereo vision are easily affected by object occlusion, viewpoint changing and scale changing in 3D scene, there will be problems such as poor detection accuracy and robustness. LiDAR point cloud can provide 3D scene information, so using deep learning method to complete 3D object detection based on LiDAR point cloud has become a research hotspot in the field of 3D vision. Aiming at the 3D object detection based on LiDAR point cloud, the relevant research in recent years is reviewed. Firstly, the 3D object detection methods based on LiDAR point cloud are divided into point cloud based, point cloud projection based, point cloud voxelization based and multi-modal fusion based 3D object detection methods according to the data form of network input, and the most representative methods in each category are described in detail. Then common datasets are introduced, and the performance of representative methods is evaluated, and the advantages and limitations of each method are discussed from several aspects. Finally, the shortcomings and difficulties are given, and the future development directions are also discussed and put forward.

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Camera images and LiDAR point clouds can provide complementary information for 3 D object detection, but how to perform effective fusion remains a challenge. To address the alignment bias problem caused by the undifferentiated fusion in traditional methods, this paper proposes an adaptive fusion network in this paper. First, construct the attention affinity matrix between the point cloud voxels and the corresponding multiple image pixels, and then achieve the importance differentiation fusion from multiple pixels to single voxels based on the affinity matrix. In addition, for the problem that it is difficult to enumerate all directions in the traditional Anchor-based detection method, this paper represents the target as key points and first performs center localization, and then regression to other attributes such as 3 D size and orientation. At the same time, to address the problem that the sample size of the center is too small for key point detection, this paper uses an elliptical Gaussian heat map for the redistribution of center samples. This algorithm improves 2.3%, 5.9%, and 4.0% LEVEL2 mAPH over the baseline PointPillar, CenterPoint, and 3 D-MAN, respectively, on the Waymo dataset [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Point cloud 3D object detection has low detection accuracy for small objects such as pedestrians and bicycles, which is easy tomiss detection and false detection.A3Dobject detectionmethodMSPT-RCNN (multi-scale point transformer-RCNN)based on multi-scale point cloud transformer is proposed to improve the detection accuracy of point cloud 3D objects. The method consists of two stages, the first stage (RPN)and the second stage (RCNN). In RPN stage, point cloud features are extracted through multi-scale transformer network, which includes multi-scale neighborhood embedding module and jump connection offset attention module to obtain multi-scale neighborhood geometric information and different levels of global semantic information, and generate high-quality initial 3D bounding box. In the RCNN stage, the multi-scale neighborhood geometric information of point cloud in the bounding box is introduced to optimize the position, size, orientation and confidence of the bounding box. The experimental results show that this method (MSPT-RCNN)has high detection accuracy, especially for distant and small objects. MSPT-RCNN can effectively improve the accuracy of 3D object detection by effectively learning the multi-scale geometric information in point cloud data and extracting different levels of effective semantic information. [ABSTRACT FROM AUTHOR]

To improve the performance of environment perception of autonomous vehicles and enhance the ability of monocular cameras to perceive obstacle 3D and boundary information, this paper proposed a ground-aware 3D object detection algorithm. Based on an optimized Center Net model, it used DLA as the backbone network to increase the redundant information prediction of 3 D bounding box center. According to the ground-aware information of the autonomous driving scenario and combined with the pinhole camera model, it obtained the object 3D center depth information to optimize the deep network learning effect. This paper evaluated the algorithm performance using the KITTI 3 D dataset. The results show that the algorithm runs at 20 fps on the basis of ensuring the accuracy of 2D object detection, which meets the real-time requirements of autonomous driving perception. At the same time, compared with the Center Net model, there are performance improvements of 4. 4 and 4. 4% in the average orientation score and the bird eye view AP, respectively. Thus, the algorithm can improve the ability of autonomous vehicles to perceive obstacle 3D and boundary information. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)