Scale variation of objects remains one of the crucial challenges in object detection. Currently, conventional dense detectors with fixed receptive fields and label weights are not conducive to the detection of multi-scale objects. However, the design limitations of unbalanced label weights and fixed refinement for multi-scale objects and multi-tasks in these studies make it difficult to achieve better detection performance. In this paper, we propose a novel dense detector named Balanced FCOS which consists of two components: Balanced Label Assignment (BLA) and Flexible Shape-based Refinement (FSR). The BLA implements scale-balanced sample assignment by introducing reweighting factors consisting of localization and classification scores into the label assignment. Low-quality but high-weight samples can be weakened by the BLA. Furthermore, we design a cross-reweighting mechanism in the BLA to ensure score consistency between classification and localization. The FSR implements scale-balanced sample refinement by learning flexible sample points’ offsets for multi-scale objects and multi-tasks based on objects’ coarse features to get more discriminative features with appropriate receptive field. In addition, better features obtained by FSR are beneficial to get better classification and localization scores, which can be used by BLA to produce accurate label weights. Only equipped with the BLA, we can achieve 41.7/46.6 AP under R50/R101-FCOS without any additional parameters. When combining the BLA with the FSR, our Balanced FCOS achieves SOTA results among dense detectors on the COCO test-dev set. Experiments conducted on other heads (T-Head, DyHead), detectors (DINO), and datasets (AI-TOD) further demonstrate the effectiveness of our method.

Efficient representation of point clouds is fundamental for LiDAR-based 3D object detection. While recent grid-based detectors often encode point clouds into either voxels or pillars, the distinctions between these approaches remain underexplored. In this paper, we quantify the differences between the current encoding paradigms and highlight the limited vertical learning within. To tackle these limitations, we propose a hybrid detection framework named Voxel-Pillar Fusion (VPF), which synergistically combines the unique strengths of both voxels and pillars. To be concrete, we first develop a sparse voxel-pillar encoder that encodes point clouds into voxel and pillar features through 3D and 2D sparse convolutions respectively, and then introduce the Sparse Fusion Layer (SFL), facilitating bidirectional interaction between sparse voxel and pillar features. Our computationally efficient, fully sparse method can be seamlessly integrated into both dense and sparse detectors. Leveraging this powerful yet straightforward representation, VPF delivers competitive performance, achieving real-time inference speeds on the nuScenes and Waymo Open Dataset.

Multi-Agent Path Finding (MAPF) is an important issue in multi-agent cooperation. Many studies apply MultiAgent Reinforcement Learning (MARL) to solve MAPF in partially observable settings. The objective of cooperative MARL is to maximize the cumulative team reward. Nevertheless, in partially observable settings, the team reward is misleading due to unpredictable factors from the behavior and state of unobserved agents. To address this issue, we propose a Partially Observable Altruistic Q-learning (POAQL) method. POAQL considers the cumulative reward of the observed subteam instead of the whole team, where Altruistic Q-learning plays an important role in learning the subteam action value. In addition, we design a new conflict resolution without additional guidance to emphasize the cooperative nature of MARL frameworks. Experimental results show that POAQL outperforms existing reinforcement learning methods in terms of efficiency and performance.

3D multi-object tracking is one of the key technologies of autonomous driving, which aims to ensure that autonomous driving vehicles accurately perceive the movements and intentions of surrounding traffic participants. In recent years, some 3D multi-object tracking methods based on multimodality have been proposed. Although these methods improve the accuracy of object association in the tracking process, these methods are still difficult to effectively deal with the problems of feature ambiguity due to occlusion, incorrect feature alignment between different modalities, and confusion of adjacent target features caused by coarse-grained feature maps. To address these problems, we propose a new multi-modality feature learning method for 3D multi-object tracking, named TEMI-MOT, which is composed of three modules in series: the point-guided image feature sampler, the instance-aware feature encoder, and the tracking pipeline. The point-guided feature sampler realizes the alignment between the point cloud and image features, the instance-aware feature encoder fuses the aligned image features with each object's points to generate the discriminative instance-aware features, and the tracking pipeline finally outputs the results based on instance-aware features and G-IoU geometric similarities. Our approach achieves state-of-the-art results on the nuScenes dataset among the methods using CenterPoint detections. The experimental results show that the proposed method has better robustness and effectiveness for 3D multi-object tracking.

Abstract It is difficult for a receiver to intercept the signals from a radar system that can emit low probability of intercept (LPI) polyphase coded signals. The traditional Wigner Hough transform (WHT) algorithm requires a large amount of computation and takes a long time to estimate the parameters of the LPI radar polyphase coded signals. To address this problem, an iterative angle search (IAS) algorithm, which when used in combination with the WHT algorithm significantly reduces the computational cost is proposed. When the signal‐to‐noise ratio is in the range of −4 to 20 dB, the carrier frequency, number of subcodes, and number of cycles of the carrier frequency per subcode of five polyphase coded signals, namely, Frank, P1, P2, P3, and P4, are accurately estimated in simulation experiments. Based on the selected IAS algorithm parameters, the estimation accuracy of the proposed method is the same as that of the traditional WHT algorithm. However, the operation time is only 5.14% of that of the traditional method. The IAS algorithm has certain application prospects. Experiments indicate that the proposed algorithm provides excellent performance and can rapidly and accurately estimate the parameters of LPI polyphase codes.

Feature Pyramid Network (FPN) has been widely adopted to exploit multi-scale features for scale variation in object detection. However, intrinsic defects in most of the current methods with FPN make it difficult to adapt to the feature of different geometric objects. To address this issue, we introduce geometric prior into FPN to obtain more discriminative features. In this paper, we propose Geometry-aware Feature Pyramid Network (GaFPN), which mainly consists of the novel Geometry-aware Mapping Module and Geometry-aware Predictor Head.The Geometry-aware Mapping Module is proposed to make full use of all pyramid features to obtain better proposal features by the weight-generation subnetwork. The weights generation subnetwork generates fusion weight for each layer proposal features by using the geometric information of the proposal. The Geometry-aware Predictor Head introduces geometric prior into predictor head by the embedding generation network to strengthen feature representation for classification and regression. Our GaFPN can be easily extended to other two-stage object detectors with feature pyramid and applied to instance segmentation task. The proposed GaFPN significantly improves detection performance compared to baseline detectors with ResNet-50-FPN: +1.9, +2.0, +1.7, +1.3, +0.8 points Average Precision (AP) on Faster-RCNN, Cascade R-CNN, Dynamic R-CNN, SABL, and AugFPN respectively on MS COCO dataset.

In this paper, we propose a point cloud based 3D object detection framework that accounts for both contextual and local information by leveraging multi-receptive field pillars, named as MuRF-Net. Recently, common pipelines can be divided into a voxel-based feature encoder and an object detector. During the feature encoding steps, contextual information is neglected, which is critical for the 3D object detection task. Thus, the encoded features are not suitable to input to the subsequent object detector. To address this challenge, we propose the MuRF-Net with a multi-receptive field voxelization mechanism to capture both contextual and local information. After the voxelization, the voxelized points (pillars) are processed by a feature encoder, and a channel-wise feature reconfiguration module is proposed to combine the features with different receptive fields using a lateral enhanced fusion network. In addition, to handle the increase of memory and computational cost brought by multi-receptive field voxelization, a dynamic voxel encoder is applied taking advantage of the sparseness of the point cloud. Experiments on the KITTI benchmark for both 3D object and Bird's Eye View (BEV) detection tasks on car class are conducted and MuRF-Net achieved the state-of-the-art results compared with other voxel-based methods. Besides, the MuRF-Net can achieve nearly real-time speed with 20Hz.