3D Target Detection Research Articles

Improved 3D Object Detection Based on PointPillars

Despite the recent advancements in 3D object detection, the conventional 3D point cloud object detection algorithms have been found to exhibit limited accuracy for the detection of small objects. To address the challenge of poor detection of small-scale objects, this paper adopts the PointPillars algorithm as the baseline model and proposes a two-stage 3D target detection approach. As a cutting-edge solution, point cloud processing is performed using Transformer models. Additionally, a redefined attention mechanism is introduced to further enhance the detection capabilities of the algorithm. In the first stage, the algorithm uses PointPillars as the baseline model. The central concept of this algorithm is to transform the point cloud space into equal-sized columns. During the feature extraction stage, when the features from all cylinders are transformed into pseudo-images, the proposed algorithm incorporates attention mechanisms adapted from the Squeeze-and-Excitation (SE) method to emphasize and suppress feature information. Furthermore, the 2D convolution of the traditional backbone network is replaced by dynamic convolution. Concurrently, the addition of the attention mechanism further improves the feature representation ability of the network. In the second phase, the candidate frames generated in the first phase are refined using a Transformer-based approach. The proposed algorithm applies channel weighting in the decoder to enhance channel information, leading to improved detection accuracy and reduced false detections. The encoder constructs the initial point features from the candidate frames for encoding. Meanwhile, the decoder applies channel weighting to enhance the channel information, thereby improving the detection accuracy and reducing false detections. In the KITTI dataset, the experimental results verify the effectiveness of this method in small objects detection. Experimental results show that the proposed method significantly improves the detection capability of small objects compared with the baseline PointPillars. In concrete terms, in the moderate difficulty detection category, cars, pedestrians, and cyclists average precision (AP) values increased by 5.30%, 8.1%, and 10.6%, respectively. Moreover, the proposed method surpasses existing mainstream approaches in the cyclist category.

Vehicle-infrastructure cooperative 3D target detection based on Feature Prediction Atrous Spatial Pyramid Pooling Net

Vehicle-infrastructure cooperative 3D target detection based on Feature Prediction Atrous Spatial Pyramid Pooling Net

Infrastructure-assisted 3D detection networks based on camera-lidar early fusion strategy

Target detection in autonomous driving is a pivotal domain of the current research focus. The process fundamentally relies on on-board sensors tasked to identify proximate objects. However, long-range perception instability and the partial obstruction by traffic participants further complicate the challenge. These factors collectively affected the effectiveness of targeted detection from the cooperative vehicle-infrastructure system (CVIS) and urgently need to be addressed. Starting from infrastructure-side assisted detection, we propose a 3D detection network based on multi-sensor sensing. Our approach consists of three sub-networks: Diversity Balanced Feature Fusion Network (MRBNeXt), Early Multimodal Fusion Network (VBRFusion), and TwoStage Lightweight Detection Network (TSL). MRBNeXt focuses on extracting raw images fused into multilevel semantic representations to address the drawbacks of needing a rich feature-level representation; VBRFusion proposes a two-branch structure that acts on the point cloud voxelization to aggregate high-dimensional features. The point features are mapped to the sampled graphical semantic features via the coordinate features to complete the early fusion and thus improve the feature quality of a single modality. In the proposed area network, TSL enhances the sensory field processing of multidimensional features using the context-aware module in a two-stage approach to achieve fast target recognition at different scale levels. Finally, we perform comparative ablation experiments on the DAIR-V2X vehicle-infrastructure dataset. The results validated our approach and demonstrated its effectiveness and enhancement in detection accuracy at the infrastructure end compared to current state-of-the-art methods. This improvement significantly boosted the performance of 3D target detection tasks in complex traffic scenarios and provided a more robust justification for the subsequent development of vehicle-side-infrastructure-side collaborative 3D target detection.

IAE-KM3D a 3D Object Detection Method Based on an Improved KM3D Network

Deep learning-based 3D target detection methods need to solve the problem of insufficient 3D target detection accuracy. In this paper, the KM3D network is selected as the benchmark network after the experimental comparison of current mainstream algorithms, and the IAE-KM3D network algorithm based on the KM3D network is proposed. First, the Resnet V2 network is introduced, and the residual module is redesigned to improve the training capability of the new residual module with higher generalization. IBN NET is then introduced to carefully integrate instance normalization and batch normalization as building blocks to improve the model’s detection accuracy in hue- and brightness-changing scenarios without increasing time loss. Then, a parameter-free attention mechanism, Simam, is introduced to improve the detection accuracy of the model. After that, the elliptical Gaussian kernel is introduced to improve the algorithm’s ability to detect 3D targets. Finally, a new key point loss function is proposed to improve the algorithm’s ability to train. Experiments using the KITTI dataset conclude that the IAE-KM3D network model significantly improves detection accuracy and outperforms the KM3D algorithm regarding detection performance compared to the original KM3D network. The improvements for AP2D, AP3D, and APBEV are 5%, 12.5%, and 8.3%, respectively, and only a tiny amount of time loss and network parameters are added. Compared with other mainstream target detection algorithms, Monn3D, 3DOP, GS3D, and FQNet, the improved IAE-KM3D network in this paper significantly improves AP3D and APBEV, with fewer network parameters and shorter time consumption.

PAFNet: Pillar Attention Fusion Network for Vehicle–Infrastructure Cooperative Target Detection Using LiDAR

With the development of autonomous driving, consensus is gradually forming around vehicle–infrastructure cooperative (VIC) autonomous driving. The VIC environment-sensing system uses roadside sensors in collaboration with automotive sensors to capture traffic target information symmetrically from both the roadside and the vehicle, thus extending the perception capabilities of autonomous driving vehicles. However, the current target detection accuracy for feature fusion based on roadside LiDAR and automotive LiDAR is relatively low, making it difficult to satisfy the sensing requirements of autonomous vehicles. This paper proposes PAFNet, a VIC pillar attention fusion network for target detection, aimed at improving LiDAR target detection accuracy under feature fusion. The proposed spatial and temporal cooperative fusion preprocessing method ensures the accuracy of the fused features through frame matching and coordinate transformation of the point cloud. In addition, this paper introduces the first anchor-free method for 3D target detection for VIC feature fusion, using a centroid-based approach for target detection. In the feature fusion stage, we propose the grid attention feature fusion method. This method uses the spatial feature attention mechanism to fuse the roadside and vehicle-side features. The experiment on the DAIR-V2X-C dataset shows that PAFNet achieved a 6.92% higher detection accuracy in 3D target detection than FFNet in urban scenes.

Research on PointPillars Algorithm Based on Feature-Enhanced Backbone Network

In the industrial field, the 3D target detection algorithm PointPillars has gained popularity. Improving target detection accuracy while maintaining high efficiency has been a significant challenge. To address the issue of low target detection accuracy in the PointPillars 3D target detection algorithm, this paper proposes an algorithm based on feature enhancement to improve the backbone network. The algorithm enhances preliminary feature information of the backbone network by modifying it based on PointPillars with the aid of channel attention and spatial attention mechanisms. To address the inefficiency caused by the excessive number of subsampled parameters in PointPillars, FasterNet (a lightweight and efficient feature extraction network) is utilized for down-sampling and forming different scale feature maps. To prevent the loss and blurring of extracted features resulting from the use of inverse convolution, we utilize the lightweight and efficient up-sampling modules Carafe and Dysample for adjusting resolution. Experimental results indicate improved accuracy under all difficulties of the KITTI dataset, demonstrating the superiority of the algorithm over PointPillars.

An overview of the development of key technologies in autonomous driving technology

Technological advances in each of the smaller fields of automated driving technology have collectively advanced the maturity of automated driving technology. This paper describes some of the technological advances in the development of automated driving of automobiles. Firstly, it introduces the improvement of 3D target detection algorithms, the problem of 3D target detection from LIDAR point cloud, and the proposed control network to address the lack of human driving logic in the existing driverless strategy. Next, the convenience of virtual simulation technology in testing self-driving cars is presented as well as the use of high-definition maps by self-driving cars to understand the road and surroundings. Then the solution to the problem of weak feature extraction and low training efficiency when extracting data in the self-driving learning model, the path planning technology for self-driving cars, and the understanding of the scene when self-driving cars are traveling at night are presented. Finally, image target recognition based on synthetic aperture radar, image color recognition method based on deep learning, and how deep learning can be applied to the field of image recognition are presented.

Laser Radar 3D Target Detection Based on Improved PointPillars

Laser Radar 3D Target Detection Based on Improved PointPillars

3D Target Detection Algorithm of Laser Point Cloud Based on Artificial Intelligence

3D Target Detection Algorithm of Laser Point Cloud Based on Artificial Intelligence

3D Target Detection Incorporating Point Cloud Columnarization and Attention Mechanisms in Intelligent Driving Systems

3D Target Detection Incorporating Point Cloud Columnarization and Attention Mechanisms in Intelligent Driving Systems

Research and Implementation of 3D Object Detection Based on Autonomous Driving Scenarios

Perception function, as an important part of autonomous driving, ensures the safety and intelligence of driving. We collect the surrounding environment of driving through hardware sensors, providing a basis for subsequent decision-making of autonomous driving. In recent years, deep learning has made breakthrough progress in object detection. Based on this, this paper uses lidar point cloud data, combined with deep learning theory and method, to carry out three-dimensional target detection tasks around lidar point cloud data, and carries out theoretical analysis, method verification, and result analysis. A 3D object detection method based on LiDAR point cloud data is proposed. The backbone network of the network model corresponding to the method is VoxelNet’s backbone network. After outputting the feature matrix, sparse point clouds are supplemented with point clouds, and then the feature matrix is decoded to generate candidate boxes. The model used in this paper on the KITTI data set can effectively solve the problem of 3D target detection in the process of autonomous driving and has good performance.

Multiattention Mechanism 3D Object Detection Algorithm Based on RGB and LiDAR Fusion for Intelligent Driving.

This paper proposes a multimodal fusion 3D target detection algorithm based on the attention mechanism to improve the performance of 3D target detection. The algorithm utilizes point cloud data and information from the camera. For image feature extraction, the ResNet50 + FPN architecture extracts features at four levels. Point cloud feature extraction employs the voxel method and FCN to extract point and voxel features. The fusion of image and point cloud features is achieved through regional point fusion and voxel fusion methods. After information fusion, the Coordinate and SimAM attention mechanisms extract fusion features at a deep level. The algorithm's performance is evaluated using the DAIR-V2X dataset. The results show that compared to the Part-A2 algorithm; the proposed algorithm improves the mAP value by 7.9% in the BEV view and 7.8% in the 3D view at IOU = 0.5 (cars) and IOU = 0.25 (pedestrians and cyclists). At IOU = 0.7 (cars) and IOU = 0.5 (pedestrians and cyclists), the mAP value of the SECOND algorithm is improved by 5.4% in the BEV view and 4.3% in the 3D view, compared to other comparison algorithms.

Monocular 3D Object Detection Using Depth Fusion

It is an important task to estimate a 3D bounding box from monocular images for autonomous driving. However, the monocular pictures do not have distance information, so it is difficult to acquire accurate results. For the sake of solving the trouble of low accuracy of the monocular image in 3D target detection because of lacking distance information, an improved monocular three-dimensional target detection algorithm based on GUPNet and neural network was proposed to promote the precision of target detection. First, based on the geometric method proposed by GUPNet, the depth, and uncertainty are obtained by direct regression using a neural network. According to the difference in the accuracy of the two methods, a parameter α was introduced, and their depth scores are obtained from the uncertainty. According to the depth score and parameter α, the depth obtained by the two methods is fused to get the final depth. Test results prove that the proposed algorithm promotes average detection precision of KITTI data set in simple, medium, and difficult cases.

3D target detection and spectral classification for single-photon LiDAR data.

3D single-photon LiDAR imaging has an important role in many applications. However, full deployment of this modality will require the analysis of low signal to noise ratio target returns and very high volume of data. This is particularly evident when imaging through obscurants or in high ambient background light conditions. This paper proposes a multiscale approach for 3D surface detection from the photon timing histogram to permit a significant reduction in data volume. The resulting surfaces are background-free and can be used to infer depth and reflectivity information about the target. We demonstrate this by proposing a hierarchical Bayesian model for 3D reconstruction and spectral classification of multispectral single-photon LiDAR data. The reconstruction method promotes spatial correlation between point-cloud estimates and uses a coordinate gradient descent algorithm for parameter estimation. Results on simulated and real data show the benefits of the proposed target detection and reconstruction approaches when compared to state-of-the-art processing algorithms.

P‐2.22: Research on 3D Target Detection Algorithm in Automatic Driving Scenario

As a research hotspot in computer vision, 3D object detection has wide application prospects. In the field of autonomous driving, the perception system with three-dimensional object detection function enables the vehicle to perceive the surrounding environment, make the vehicle more intelligent, and play a role in assisting driving. Aiming at two commonly used sensors: monocular camera and lidar, combined with deep learning, this paper separately studies the 3D target detection algorithm of PointPillars based on attention mechanism and the 3D target detection method based on monocular vision. The results show that the detection performance based on monocular vision still has advantages and can be used as an effective supplement to Lidar based three-dimensional target detection, improving the robustness of the sensing system.

FP-RCNN: A Real-Time 3D Target Detection Model based on Multiple Foreground Point Sampling for Autonomous Driving

FP-RCNN: A Real-Time 3D Target Detection Model based on Multiple Foreground Point Sampling for Autonomous Driving

MFF-Net: Multimodal Feature Fusion Network for 3D Object Detection

In complex traffic environment scenarios, it is very important for autonomous vehicles to accurately perceive the dynamic information of other vehicles around the vehicle in advance. The accuracy of 3D object detection will be affected by problems such as illumination changes, object occlusion, and object detection distance. To this purpose, we face these challenges by proposing a multimodal feature fusion network for 3D object detection (MFF-Net). In this research, this paper first uses the spatial transformation projection algorithm to map the image features into the feature space, so that the image features are in the same spatial dimension when fused with the point cloud features. Then, feature channel weighting is performed using an adaptive expression augmentation fusion network to enhance important network features, suppress useless features, and increase the directionality of the network to features. Finally, this paper increases the probability of false detection and missed detection in the non-maximum suppression algorithm by increasing the one-dimensional threshold. So far, this paper has constructed a complete 3D target detection network based on multimodal feature fusion. The experimental results show that the proposed achieves an average accuracy of 82.60% on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset, outperforming previous state-of-the-art multimodal fusion networks. In Easy, Moderate, and hard evaluation indicators, the accuracy rate of this paper reaches 90.96%, 81.46%, and 75.39%. This shows that the MFF-Net network has good performance in 3D object detection.

Research on 3D Target Detection Algorithm Based on PointFusion Algorithm Improvement

Research on 3D Target Detection Algorithm Based on PointFusion Algorithm Improvement

Infrastructure 3D Target Detection Based on Multi-Mode Fusion for Intelligent and Connected Vehicles

Infrastructure 3D Target Detection Based on Multi-Mode Fusion for Intelligent and Connected Vehicles

Lidar 3D Target Detection Based on Improved PointPillars

Lidar 3D Target Detection Based on Improved PointPillars