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Airport runway segmentation can effectively reduce the accident rate during the landing phase, which has the largest risk of flight accidents. With the rapid development of deep learning (DL), related methods achieve good performance on segmentation tasks and can be well adapted to complex scenes. However, the lack of large-scale, publicly available datasets in this field makes the development of methods based on DL difficult. Therefore, we propose a benchmark for airport runway segmentation, named BARS. Additionally, a semiautomatic annotation pipeline is designed to reduce the annotation workload. BARS has the largest dataset with the richest categories and the only instance annotation in the field. The dataset, which was collected using the X-Plane simulation platform, contains 10,256 images and 30,201 instances with three categories. We evaluate eleven representative instance segmentation methods on BARS and analyze their performance. Based on the characteristic of an airport runway with a regular shape, we propose a plug-and-play smoothing postprocessing module (SPM) and a contour point constraint loss (CPCL) function to smooth segmentation results for mask-based and contour-based methods, respectively. Furthermore, a novel evaluation metric named average smoothness (AS) is developed to measure smoothness. The experiments show that existing instance segmentation methods can achieve prediction results with good performance on BARS. SPM and CPCL can effectively enhance the AS metric while modestly improving accuracy. Our work will be available at https://github.com/c-wenhui/BARS. [ABSTRACT FROM AUTHOR]

Dimension measurement is of utmost importance in the logistics industry. This work studies a hand-held structured light vision system for boxes. This system measures dimension information through laser triangulation and deep learning using only two laser-box images from a camera and a cross-line laser projector. The structured edge maps of the boxes are detected by a novel end-to-end deep learning model based on a trimmed-holistically nested edge detection network. The precise geometry of the box is calculated by the 3D coordinates of the key points in the laser-box image through laser triangulation. An optimization method for effectively calibrating the system through the maximum likelihood estimation is then proposed. Results show that the proposed key point detection algorithm and the designed laser-vision-based visual system can locate and perform dimension measurement of measured boxes with high accuracy and reliability. The experimental outcomes show that the system is suitable for portable automatic box dimension online measurement. [ABSTRACT FROM AUTHOR]

Point cloud registration is used to find a rigid transformation from the source point cloud to the target point cloud. The main challenge in the point cloud registration is in finding correct correspondences in complex scenes that may contain many noise and repetitive structures. At present, many existing methods use outlier rejections to help the network obtain more accurate correspondences, but they often ignore the spatial consistency between keypoints. Therefore, to address this issue, we propose a spatial consistency guided network using contrastive learning for point cloud registration (SCRnet), in which its overall stage is symmetrical. SCRnet consists of four blocks, namely feature extraction block, confidence estimation block, contrastive learning block and registration block. Firstly, we use m i n i - P o i n t N e t to extract coarse local and global features. Secondly, we propose confidence estimation block, which formulate outlier rejection as confidence estimation problem of keypoint correspondences. In addition, the local spatial features are encoded into the confidence estimation block, which makes the correspondence possess local spatial consistency. Moreover, we propose contrastive learning block by constructing positive point pairs and hard negative point pairs and using Point-Pair-INfoNCE contrastive loss, which can further remove hard outliers through global spatial consistency. Finally, the proposed registration block selects a set of matching points with high spatial consistency and uses these matching sets to calculate multiple transformations, then the best transformation can be identified by initial alignment and Iterative Closest Point (ICP) algorithm. Extensive experiments are conducted on KITTI and nuScenes dataset, which demonstrate the high accuracy and strong robustness of SCRnet on point cloud registration task. [ABSTRACT FROM AUTHOR]

The one-shot multiple object tracking (MOT) framework has drawn more and more attention in the MOT research community due to its advantage in inference speed. However, the tracking accuracy of current one-shot approaches could lead to an inferior performance compared with their two-stage counterparts. The reasons are two-fold: one is that motion information is often neglected due to the single-image input. The other is that detection and re-identification (ReID) are two different tasks with different focuses. Joining detection and re-identification at the training stage could lead to a suboptimal performance. To alleviate the above limitations, we propose a one-shot network named Motion and Correlation-Multiple Object Tracking (MAC-MOT). MAC-MOT introduces a motion enhance attention module (MEA) and a dual correlation attention module (DCA). MEA performs differences on adjacent feature maps which enhances the motion-related features while suppressing irrelevant information. The DCA module focuses on decoupling the detection task and re-identification task to strike a balance and reduce the competition between these two tasks. Moreover, symmetry is a core design idea in our proposed framework which is reflected in Siamese-based deep learning backbone networks, the input of dual stream images, as well as a dual correlation attention module. Our proposed approach is evaluated on the popular multiple object tracking benchmarks MOT16 and MOT17. We demonstrate that the proposed MAC-MOT can achieve a better performance than the baseline state of the arts (SOTAs). [ABSTRACT FROM AUTHOR]

Geometric feature extraction of 3D point clouds plays an important role in many 3D computer vision applications such as region labeling, 3D reconstruction, object segmentation, and recognition. However, hand-designed features on point clouds lack semantic information, so cannot meet these requirements. In this paper, we propose local feature extraction network (LFE-Net) which focus on extracting local feature for point clouds analysis. Such geometric features learning from a relation of local points can be used in a variety of shape analysis problems such as classification, part segmentation, and point matching. LFE-Net consists of local geometric relation (LGR) module which aims to learn a high-dimensional local feature to express the relation between points and their neighbors. Benefiting from the additional singular values of local points and hierarchical neural networks, the learned local features are robust to permutation and rigid transformation so that they can be transformed into 3D descriptors. Moreover, we embed prior spatial information of the local points into the sub-features for combining features from multiple levels. LFE-Net achieves state-of-the-art performances on standard benchmarks including ModelNet40, ShapeNetPart. [ABSTRACT FROM AUTHOR]

Portable box volume measurement has always been a popular issue in the intelligent logistic industry. This work presents a portable system for box volume measurement that is based on line-structured light vision and deep learning. This system consists of a novel 2 × 2 laser line grid projector, a sensor, and software modules, with which only two laser-modulated images of boxes are required for volume measurement. For laser-modulated images, a novel end-to-end deep learning model is proposed by using an improved holistically nested edge detection network to extract edges. Furthermore, an automatic one-step calibration method for the line-structured light projector is designed for fast calibration. The experimental results show that the measuring range of our proposed system is 100–1800 mm, with errors less than ±5.0 mm. Theoretical analysis indicates that within the measuring range of the system, the measurement uncertainty of the measuring device is ±0.52 mm to ±4.0 mm, which is consistent with the experimental results. The device size is 140 mm × 35 mm × 35 mm and the weight is 110 g, thus the system is suitable for portable automatic box volume measurement. [ABSTRACT FROM AUTHOR]