X-ray image plays an important role in manufacturing industry for quality assurance, because it can reflect the internal condition of weld region. However, the shape and scale of different defect types vary greatly, which makes it challenging for model to detect weld defects. In this article, we propose a weld defect detection method based on convolution neural network (CNN), namely, lighter and faster YOLO (LF-YOLO). In particular, a reinforced multiscale feature (RMF) module is designed to implement both parameter-based and parameter-free multiscale information extracting operations. RMF enables the extracted feature map to represent more plentiful information, which is achieved by a superior hierarchical fusion structure. To improve the performance of detection network, we propose an efficient feature extraction (EFE) module. EFE processes input data with extremely low consumption and improves the practicability of whole network in actual industry. Experimental results show that our weld defect detection network achieves satisfactory balance between performance and consumption and reaches 92.9 mean average precision (mAP50) with 61.5 frames/s. To further prove the ability of our method, we test it on the public dataset MS COCO, and the results show that our LF-YOLO has an outstanding versatility detection performance. The code is available at https://github.com/lmomoy/LF-YOLO .

To achieve more accurate perception performance, LiDAR and camera are gradually chosen to improve 3D object detection simultaneously. However, it is still a non-trivial task to build an effective fusion mechanism, and this is hindering the development of multi-modal based method. Especially, the mapping relationship construction between two modalities is far from fully explored. Canonical cross-modal mapping suffers from failure when the calibration matrix is incorrect, and it also greatly wastes the amount and density of RGB image information. This paper aims to extend the traditional one-to-one alignment relationship between LiDAR and camera. For all projected point clouds, we enhance their cross-modal mapping relationship through aggregating color-texture related feature and shape-contour related feature. Further, a mapping pyramid is proposed to leverage the semantic representation of the image feature at different stages. Based on the above mapping enhancement strategies, our method increases the engagement rate of image. Finally, we design a fusion module based on an attention mechanism to improve the point cloud feature with the auxiliary image feature. Extensive experiments on the KITTI dataset and SUN-RGBD dataset show that our model achieves satisfactory 3D object detection, especially for categories with sparse point clouds compared with other multi-modal fusion networks.

The mobile robot localization methodologies in common use at present have been introduced. A localization algorithm based on extended Kalman filter (EKF) has been proposed on the basis of environment feature extraction and map building, which can reduce the error in the calculation of the robot's position and orientation. The method is that the mobile robot analyses and fuses the messages in surroundings from multiple sensors by EKF theory, which enables the robot to identify the surrounding objects clearly and guide itself successfully. The simulation and experimental results show that the proposed localization method is effective

6D target object detection is of great importance to many applications such as robotics, industrial automation, and unmanned vehicles and is increasingly influencing broad industries including manufacturing, transportation, and retail industries, to name a few. This paper focuses on detecting the 6D poses of the target objects from the point cloud of a cluttered scene. However, conventional point cloud-based 6D object detection methods rely on the robustness of key-point detection results that are not straightforward for humans to understand. The drawback makes conventional point cloud-based methods require expert knowledge to tune. In this paper, we introduced a 6D target object detection method that uses segmented object point cloud patches instead of key points to predict object 6D poses and identity. Our main contributions are an end-to-end data-driven pose correction model that is enhanced with a novel simple yet efficient basis spanning layer booster. Experiments show that although the proposed model is trained only using object CAD models, its 6D detection performance matches that of the models using view data. Thus, the proposed method is suitable for 6D detection applications that have object CAD models instead of labeled scene data.

Register control systems are a necessary technical measure and an important means of large-scale roll-to-roll industrial printing production. They are effective in reducing the generation of unqualified printing products and improving the accuracy and stability of industrial printing production lines. In this paper, we propose a new register control algorithm that effectively improves the detection accuracy and compensation speed of printing register control systems. Firstly, according to the principle of register color mark detection, a new pulse-width subdivision detection algorithm is proposed, which can greatly improve the accuracy of register error detection without increasing the resolution of external encoder pulse signals. Then, the optimized error compensation method can adaptively adjust the corresponding system parameters, such as the output intensity, error filtering period and output period, according to the current register error size. The new output method greatly improves the error compensation speed without increasing error oscillation. Lastly, the experimental results of 0.01 mm show that the proposed control approach significantly improves the detection accuracy of register errors. Furthermore, the new control algorithm can be applied in the industrial field.

The inherent sparsity of LiDAR data often leads to extremely sparse depth maps, which poses a challenge for the development of LiDAR-based egocentric vehicles, such as self-driving cars and mobile robots. To overcome this limitation, guided depth completion methods use calibrated camera images to create precise, dense depth predictions from sparse LiDAR data. However, the extreme reliance on camera image limits its generalization of guided depth completion, especially its robustness to weather and light. In this paper, we aim to utilize camera images only during training phase to improve unguided depth completion, and discard camera in the inference phase. We comprehensively analyze the pivotal role of camera images in the depth completion task and emphasize the significance of the frequency distribution within the local windows, quantitatively demonstrating its substantial contribution. Subsequently, we introduce cross-modality knowledge distillation to align LiDAR features with camera features in the frequency domain, yielding corresponding guidance features. We devise a guidance and selection module to mitigate unavoidable inaccuracies in knowledge distillation, while it can enhance depth features and adeptly selects more precise encoded values from both the guidance branch and the unguided input. To further refine the completion result, we propose a progressive depth completion module incorporating two sub-networks connected by an attention for refinement module. This module produces weighted features from the decoder of the first stage to enhance the features in the encoder of the second stage. We denominate our method as Better Unguided Network (BUNet) and evaluate its efficacy on the KITTI depth completion benchmark and NYUv2 dataset, demonstrating its superiority over methods that exclude camera images during the inference phase.