Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

6G network enables the rapid connection of autonomous vehicles, the generated internet of vehicles establishes a large-scale point cloud, which requires automatic point cloud analysis to build an intelligent transportation system in terms of the 3D object detection and segmentation. Recently, a great variety of deep convolution networks have been proposed for 3D data analysis, making significant progress in the application of deep learning in 3D computer vision. Inspired by the application of transformer network in 2D computer visual tasks, and in order to increase the expression ability of local fine-grained features, we propose an effective local feature transformer network to learn local feature information and correlations between point clouds. Our network is adaptive to the arrangement of set elements through transformer module, so it is suitable for the feature extraction of local point clouds. In addition, experimental results demonstrate that our LFT-network outperforms the state-of-the-art in 3D model classification tasks on ModelNet40 dataset and segmentation tasks on S3DIS dataset.

With various emerging mobile devices, the visual content have be to resized into different sizes or aspect ratios for good viewing experiences. In this paper, we propose a new multioperator retargeting algorithm by using four retargeting operators of seam carving, cropping, warping, and scaling iteratively. To determine which retargeting operator should be used at each iteration, we adopt structural similarity (SSIM) to evaluate the similarity between the original and retargeted images. The retargeting operator sequence is constructed based on the four types of retargeting operators by an optimization process. Since the sizes of original and retargeted images are different, scale-invariant feature transform flow is used for dense correspondence between the original and retargeted images for similarity evaluation. Additionally, visual saliency is used to weight SSIM results based on the characteristics of the human visual system. Experimental results on a public image retargeting database have shown the promising performance of the proposed multioperator retargeting algorithm.

In the context of autonomous driving environment perception, multi-modal fusion plays a pivotal role in enhancing robustness, completeness, and accuracy, thereby extending the performance boundary of the perception system. However, directly applying LiDAR-related algorithms to radar and camera fusion leads to significant challenges, such as radar sparsity, absence of height information, and noise, resulting in substantial performance loss. To address these issues, our proposed method, SparseFusion3D, utilizes a dual-branch feature-level fusion network that fully models sensor interactions, effectively mitigating the adverse effects of radar sparsity and noise on modality association. Additionally, to enhance modal correlations and accuracy while alleviating radar point cloud sparsity and measurement ambiguity, we introduce MSPCP, which compensates for point cloud offset. Moreover, we integrate Radar Painter to leverage image information and further enhance MSPCP. SparseFusion3D exhibits competitive performance compared to previous radar-camera fusion models, achieving approximately 1.5x inference speedup with similar performance to dense query methods, while also improving by 20.1% compared to the baseline approach.