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 .

Real-time vision based system of fault detection (RVBS-FD) for freight trains aims to complete routine maintenance tasks efficiently for ensuring railway transportation security. However, most existing systems are designed to detect only one specific type of faults or even one fault, which fail to deal with multi-fault detection. Recently, the rapid development of deep learning techniques enables systems to provide a robust solution for the RVBS-FD of freight trains. But general convolutional neural networks (CNN) cannot fully meet the actual requirements in terms of the real-time, accuracy, and resource constrains for the RVBS-FD of freight trains. To solve these problems, we propose a CNN-based detector called Light FTI-FDet for the RVBS-FD of freight train. First, we use the multi-region proposal networks which extract a set of prior bounding boxes to achieve initial fault proposal generation. Then, a powerful multi-level region-of-interest pooling is presented for proposal classification and accurate detection. We finally design a reliable model reduction scheme to pursue fast speed with high detection accuracy in a simple manner. The experimental results on five typical fault benchmarks indicate that our Light FTI-FDet achieves higher accuracy and fast speed with about 17% model size of the well-known Faster R-CNN detector, substantially outperforming state-of-the-art methods.

Real-time fault detection for freight trains plays a vital role in guaranteeing the security and optimal operation of railway transportation under stringent resource requirements. Despite the promising results for deep-learning-based approaches, the performance of these fault detectors on freight train images is far from satisfactory in both accuracy and efficiency. This article proposes a unified light framework to improve detection accuracy while supporting a real-time operation with a low-resource requirement. We first design a novel lightweight backbone (real-time fault detection network-RFDNet) to improve the accuracy and reduce computational cost. Then, we propose a multiregion proposal network using multiscale feature maps generated from the RFDNet to improve the detection performance. Finally, we present multilevel position-sensitive score maps and region of interest pooling to further improve accuracy with few redundant computations. Extensive experimental results on public benchmark datasets suggest that our RFDNet can significantly improve the performance of the baseline network with higher accuracy and efficiency. Experiments on six fault datasets show that our method is capable of real-time detection at over 38 frames/s and achieves competitive accuracy and lower computation than the state-of-the-art detectors.

This paper proposes an affinity fusion graph framework to effectively connect different graphs with highly discriminating power and nonlinearity for natural image segmentation. The proposed framework combines adjacency-graphs and kernel spectral clustering based graphs (KSC-graphs) according to a new definition named affinity nodes of multi-scale superpixels. These affinity nodes are selected based on a better affiliation of superpixels, namely subspace-preserving representation which is generated by sparse subspace clustering based on subspace pursuit. Then a KSC-graph is built via a novel kernel spectral clustering to explore the nonlinear relationships among these affinity nodes. Moreover, an adjacency-graph at each scale is constructed, which is further used to update the proposed KSC-graph at affinity nodes. The fusion graph is built across different scales, and it is partitioned to obtain final segmentation result. Experimental results on the Berkeley segmentation dataset and Microsoft Research Cambridge dataset show the superiority of our framework in comparison with the state-of-the-art methods. The code is available at https://github.com/Yangzhangcst/AF-graph.

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.