Bridge crack detection is essential to ensure bridge safety. The introduction of deep learning technology has made it possible to detect bridge cracks automatically and accurately. In this study, the Inception-Resnet-v2 algorithm was systematically improved and applied to the real-time detection of bridge cracks. We propose an end-to-end bridge crack detection model based on a convolutional neural network. This model combines the advantages of Inception convolution and residual networks, broadening the network width and alleviating the training problem of the deep network. The calculation speed is improved while still ensuring accuracy. Multi-scale feature fusion enables the network to extract contextual information of different scales, which improves the accuracy of crack recognition. The GKA (K-means clustering method based on a genetic algorithm) realizes the accurate segmentation of the target area, greatly enhances the clustering effect, and effectively improves the detection speed. In this model, large fracture datasets are used for training and testing without pre-training. The experimental results show that the performance of this method was improved in all aspects: accuracy, 99.24%; recall, 99.03%; F-measure, 98.79%; and FPS(Frames Per Second), 196. INDEX TERMS bridge crack detection, Inception-Resnet-v2, multiscale feature fusion, GKAThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

Bearing fault is a process of gradual development and deepening. In the early stage of the fault, if it can be found out in time and taken reasonable prevention and elimination measures, we can avoid serious losses and safety accidents. Therefore, the feature extraction and analysis of early weak fault has important practical significance. In this paper, an improved multiscale permutation entropy (IMPE) method was proposed to overcome the shortcomings in the coarse-grained process. In order to solve the problem that only considering a single coarse-grained sequence under a certain scale may lead to the loss of feature information, this paper proposed to calculate the time series with equal overlapping segments, that was to consider all coarse-grained sequences under the same scale to reflect the feature information of fault signals more comprehensively. In order to solve the problem that feature extraction is not refined enough when using the first-order moment mean value calculation in traditional MPE calculation, a calculation method based on the skewness of the third-order moment was proposed. The calculation method is more sensitive to the complexity and fluctuation of signals and can better describe the feature details and extract the fault features effectively. IMPE was applied to feature extraction of early weak fault of rolling bearing and input into Support Vector Machines (SVMs) for faults classification. Aiming at SVM parameter optimization problem, an improved chaos firefly optimization algorithm was proposed. Experimental results show that the new method of early weak fault identification based on IMPE-SVM was effective in detecting rolling bearing faults with different severity.

Underwater images have low quality, and underwater targets have different sizes. The mainstream target detection networks cannot achieve good results in detecting objects from underwater images. In this study, a lightweight underwater multiscale target detection model with an attention mechanism is designed to solve the above problems. In this model, MobileNetv3 is used as the backbone network for preliminary feature extraction. The lightweight feature extraction module (LFEM) pays attention to the feature map at the channel and space levels. The features with large weights are promoted, while the features with small weights are suppressed. Meanwhile, cross-group information exchange enriches the semantic information and location information of the objects. The context aggregation module (CIAM) pools the extracted feature maps to obtain feature pyramids, and it uses the upsampling-feature refinement-cascade addition (URC) method to effectively fuse global context information and enhance the feature representation. The scale normalization for feature pyramids (SNFP) performs adaptive multiscale perception and multianchor detection on feature maps to cover objects of different sizes and realize multiscale object detection in underwater images. The proposed network can realize lightweight feature extraction, effectively handle the global relationship between the underwater scene and the object while expanding the receptive field, traverse the objects of different scales, and achieve adaptive multianchor detection of multiscale objects in underwater images. The experimental results indicate that our method achieves an average accuracy of 81.94% and a detection speed of 44.3 FPS on a composite dataset. Also, our method is better than the mainstream object detection networks in terms of detection accuracy, lightweight design, and real-time performance.

Owing to the development of computerized vision technology, object detection based on convolutional neural networks is being widely used in the field of bridge crack detection. However, these networks have limited utility in bridge crack detection because of low precision and poor real-time performance. In this study, an improved single-shot multi-box detector (SSD) called ISSD is proposed, which seamlessly combines the depth separable deformation convolution module (DSDCM), inception module (IM), and feature recalibration module (FRM) in a tightly coupled manner to tackle the challenges of bridge crack detection. Specifically, DSDCM was utilized for extracting the characteristic information of irregularly shaped bridge cracks. IM was designed to expand the width of the network, reduce network calculations, and improve network computing speed. The FRM was employed to determine the importance of each feature channel through learning, enhance the useful features according to their importance, and suppress the features that are insignificant for bridge crack detection. The experimental results demonstrated that ISSD is effective in bridge crack detection tasks and offers competitive performance compared to state-of-the-art networks.

The remote sensing images in large scenes have a complex background, and the types, sizes, and postures of the targets are different, making object detection in remote sensing images difficult. To solve this problem, an end-to-end multi-size object detection method based on a dual attention mechanism is proposed in this paper. First, the MobileNets backbone network is used to extract multi-layer features of remote sensing images as the input of MFCA, a multi-size feature concentration attention module. MFCA employs an attention mechanism to suppress noise, enhance effective feature reuse, and improve the adaptability of the network to multi-size target features through multi-layer convolution operation. Then, TSDFF (two-stage deep feature fusion module)deeply fuses the feature maps output by MFCA to maximize the correlation between the feature sets and especially improve the feature expression of small targets. Next, the GLCNet (global-local context network) and the SSA (significant simple attention module) distinguish the fused features and screen out useful channel information, which makes the detected features more representative. Finally, the loss function is improved to truly reflect the difference between the candidate frames and the real frames, enhancing the network's ability to predict complex samples. The performance of our proposed method is compared with other advanced algorithms on NWPU VHR-10, DOTA, RSOD open datasets. Experimental results show that our proposed method achieves the best AP (average precision) and mAP (mean average precision), indicating that the method can accurately detect multi-type, multi-size, and multi-posture targets with high adaptability.