ELLK-Net, an efficient, lightweight network with a large kernel, is proposed for SAR ship detection. It addresses background variations, different ship scales, and noise interference challenges. ELLK-Net uses an anchor-free detector framework and sequentially decomposes large kernel convolutions to capture comprehensive global information and long-range dependencies. It adaptively selects convolution kernels on the basis of target characteristics, enhancing multiscale feature expression. A novel large kernel multiscale attention module is introduced to enhance interlayer feature fusion and semantic alignment, mitigating the impacts of overlapping ships and scattering noise. Structural reparameterization techniques optimize inference speed across devices without compromising accuracy. The experimental results on the SSDD and HRSID datasets demonstrate that ELLK-Net achieves impressive AP ₅₀ values of 95.6% and 90.6% for horizontal box detection and 89.7% and 79.7% for rotating box detection, respectively. The reparameterized detector exhibits a significant 48.7% FPS improvement on the Nvidia Jetson NX platform, indicating its suitability for edge computing deployment. The code is available at https://github.com/CHD-IPAC/ELLK-Net.

Six-degree-of-freedom (6-DOF) pose estimation from feature correspondences remains a popular and robust approach for 3-D registration. However, heavy outliers that existed in the initial correspondence set pose a great challenge to this problem. This article presents a simple yet effective estimator called SAmple Consensus by sampling COmpatibility Triangles in graphs (SAC-COT) for robust 6-DOF pose estimation and 3-D registration. The key novelty is a guided three-point sampling approach. It is based on a novel correspondence sample representation, i.e., COmpatibility Triangle (COT). We first model the correspondence set as a graph with nodes connecting compatible correspondences. Then, by ranking and sampling COTs formed by ternary loops, we show that correct hypotheses can be generated in early iteration stage. Finally, the hypothesis generated by the COT yielding to the maximum consensus is the output of SAC-COT. Extensive experiments on six data sets and extensive comparisons with the state-of-the-art estimators confirm that: 1) SAC-COT can achieve accurate registrations with a few iterations and 2) SAC-COT outperforms all competitors and is ultrarobust when confronted with Gaussian noise, data decimation, holes, clutter, partial overlap, varying scales of input correspondences, and data modality variation.

Lane detection is crucial for autonomous driving systems (ADS), utilizing sensors like cameras and LiDAR to identify lanes and understand vehicle position, direction, and lane shape. It provides data support for the control system to make informed driving decisions. In this survey, we review recent advancements in lane detection, focusing on both 2D techniques and emerging 3D methods. We begin with an overview of the significance of lane detection in ADS, followed by an analysis of the evolution of 2D techniques over the past decade, covering traditional and deep learning approaches. We also examine recent advancements in 3D lane detection. Additionally, we summarize evaluation metrics and popular datasets in the field. Finally, we discuss current challenges and future directions in lane detection, aiming to provide valuable insights for researchers and developers in this technology.

This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration. Estimating six-degree-of-freedom (6-DoF) pose from feature correspondences remains a popular approach to 3D rigid registration, where random sample consensus (RANSAC) is a well-known solution to this problem. However, existing metrics for RANSAC hypotheses are either time-consuming or sensitive to common nuisances, parameter variations, and different application scenarios, resulting in performance deterioration with respect to overall registration accuracy and speed. We alleviate this problem by first analyzing the contributions of inliers and outliers and then proposing several efficient and robust metrics with different designing motivations for RANSAC hypotheses. Comparative experiments on four standard datasets with different nuisances and application scenarios verify that our considered metrics can significantly improve the registration performance and are more robust than several state-of-the-art competitors, making them good gifts to practical applications. This work also draws an interesting conclusion, i.e., not all inliers are equal while all outliers should be equal, which may shed new light on this research problem.

A system with light detection and ranging (LiDAR) and camera (named as LiDAR-camera system) plays the essential role in intelligent vehicle (IV), for it provides 3D spatial and 2D texture features for 3D scene understanding. To leverage LiDAR point cloud and image, extrinsic calibration is a crucial technique, for it can align 2D pixel and 3D point in the pixel-level accuracy. With the rapid development of IV, calibration demand is shifted from offline to online, from the specific scenes to the open scenes. It brings new challenge to the calibration task. Although numbers of approaches have been proposed in the last decade, there lacks an in-depth summary about this topic. Thus, we conduct a survey of extrinsic calibration. Theoretically, the key of calibration is to build correspondence from LiDAR point cloud and optical image. From the viewpoint of correspondence, we attempt to divide the mainstream approaches into explicit and implicit correspondence based methods. After that, we summarize both the strength and weakness of the current works, provide the methods comparison, and list the open-source implementations. Finally, we analyze the tendency of calibration approach, discuss the remained problems in this field. We believe that this survey benefits to the community of autonomous driving.

This article presents a simple yet effective method for 3-D correspondence selection and point cloud registration. It first models the initial correspondence set as a graph with nodes representing correspondences and edges connecting geometrically compatible nodes. Such graphs offer either loose or tight geometric constraints for judging the correctness of correspondence, e.g., edges, loops, and cliques. Then, we render these constraints dynamic voters to judge the correctness of a node. More specifically, we develop a loose–tight geometric voting (LT-GV) method that employs both loose and tight geometric constraints in the graph to score 3-D feature correspondences. The motivation behind this is to strike a balanced performance in terms of precision and recall because loose and tight constraints are complementary to each other. Under the dynamic voting scheme with both loose and tight voters, consistent correspondences can be retrieved based on the voting score. Both feature-matching and 3-D point cloud registration experiments on datasets with different modalities, challenges, application scenarios, and comparisons with state-of-the-art methods (including deep learned methods) verify that our LT-GV is effective for correspondence selection, robust to a number of nuisances, and able to dramatically boost 3-D point cloud registration performance.