Towards involving the convolutional neural networks into the object detection field, many computer vision tasks have achieved favorable successes. In order to adapt targets with various scales, deep feature pyramid is widely used, since the traditional object detection methods detect different objects in Gaussian image pyramid. However, due to the mismatching between the anchors and the feature distributions of targets, the accurate detection for targets with various scales is still a challenge. Considering the differences between the theoretical receptive field and effective receptive field, we propose a novel anchor generation method, which takes the effective receptive field as the standard. The proposed method is evaluated on the PASCAL VOC dataset and shows the favorable results.

In this work, we present one novel rust detection method based upon one-class classification and L2 sparse representation (SR) with decision fusion. Firstly, a new color contrast descriptor is proposed for extracting the rust features of steel structure images. Considering that the patterns of rust features are more simplified than those of non-rust ones, one-class support vector machine (SVM) classifier and L2 SR classifier are designed with these rust image features, respectively. After that, a multiplicative fusion rule is advocated for combining the one-class SVM and L2 SR modules, thereby achieving more accurate rust detecting results. In the experiments, we conduct numerous experiments, and when compared with other developed rust detectors, the presented method can offer better rust detecting performances.

Hyperspectral images (HSIs) are generally susceptible to various noise, such as Gaussian and stripe noise. Recently, numerous denoising algorithms have been proposed to recover the HSIs. However, those approaches cannot use spectral information efficiently and suffer from the weakness of stripe noise removal. Here, we propose a tensor decomposition method with two different constraints to remove the mixed noise from HSIs. For a HSI cube, we first employ the tensor singular value decomposition (t-SVD) to effectively preserve the low-rank information of HSIs. Considering the continuity property of HSIs spectra, we design a simple smoothness constraint by using Tikhonov regularization for tensor decomposition to enhance the denoising performance. Moreover, we also design a new unidirectional total variation (TV) constraint to filter the stripe noise from HSIs. This strategy will achieve better performance for preserving images details than original TV models. The developed method is evaluated on both synthetic and real noisy HSIs, and shows the favorable results.

The concrete quality of supporting layer in ballastless track is important for the safe operation of a high-speed railway (HSR). However, the supporting layer is covered by the upper track slab and the functional layer, and it is difficult to detect concealed defects inside the supporting layer. To solve this problem, a method of elastic wave velocity imaging is proposed to analyze the concrete quality. First, the propagation path of the elastic wave in the supporting layer is analyzed, and a head-wave arrival-time (HWAT) extraction method based on the wavelet spectrum correlation analysis (WSCA) is proposed. Then, a grid model is established to analyze the relationships among the grid wave velocity, travel route, and travel time. A loss function based on the total variation is constructed, and an inverse method is applied to evaluate the elastic wave velocity in the supporting layer. Finally, simulation and field experiments are conducted to verify the suppression of noise signals and the accuracy of an inverse imaging for the elastic wave velocity estimation. The results show that the WSCA analysis could extract the HWAT efficiently, and the inverse imaging method could accurately estimate wave velocity in the supporting layer.

As a typical surface defect of tunnel lining structures, cracking disease affects the durability of tunnel structures and poses hidden dangers to tunnel driving safety. Factors such as interference from the complex service environment of the tunnel and the low signal-to-noise ratio of the crack targets themselves, have led to existing crack recognition methods based on semantic segmentation being unable to meet actual engineering needs. Based on this, this paper uses the Unet network as the basic framework for crack identification and proposes to construct a multi-kernel convolution cascade enhancement (MKCE) model to achieve accurate detection and identification of crack diseases. First of all, to ensure the performance of crack feature extraction, the model modified the main feature extraction network in the basic framework to ResNet-50 residual network. Compared with the VGG-16 network, this modification can extract richer crack detail features while reducing model parameters. Secondly, considering that the Unet network cannot effectively perceive multi-scale crack features in the skip connection stage, a multi-kernel convolution cascade enhancement module is proposed by combining a cascaded connection of multi-kernel convolution groups and multi-expansion rate dilated convolution groups. This module achieves a comprehensive perception of local details and the global content of tunnel lining cracks. In addition, to better weaken the effect of tunnel background clutter interference, a convolutional block attention calculation module is further introduced after the multi-kernel convolution cascade enhancement module, which effectively reduces the false alarm rate of crack recognition. The algorithm is tested on a large number of subway tunnel crack image datasets. The experimental results show that, compared with other crack recognition algorithms based on deep learning, the method in this paper has achieved the best results in terms of accuracy and intersection over union (IoU) indicators, which verifies the method in this paper has better applicability.