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.

Multispectral pedestrian detection based on deep learning can provide a robust and accurate detection under different illumination conditions, which has important research significance in safety. In order to reduce the log-average miss rate of the object under different illumination conditions, a new one-stage detector suitable for multispectral pedestrian detection is proposed. First, in order to realize the complementarity between the information flows of the two modalities in the feature extraction stage to reduce the object loss, a low-cost cross-modality feature complementary module (CFCM) is proposed. Second, in order to suppress the background noise in different environments and enhance the semantic information and location information of the object, so as to reduce the error detection of the object, an attention-based feature enhancement fusion module (AFEFM) is proposed. Thirdly, through the feature complementarity of color-thermal image pair and the multi-scale fusion of depth feature layer, the horizontal and vertical multi-dimensional data mining of parallel deep neural network is realized, which provides effective data support for object detection algorithm. Finally, through the reasonable arrangement of proposed modules, a robust multispectral detection framework is proposed. The experimental results on the Korea Advanced Institute of Science and Technology (KAIST) pedestrian benchmark show that the proposed method has the lowest log-average miss rate compared with other state-of-the-art multispectral pedestrian detectors, and has a good balance in speed and accuracy.

Bridge crack is one of the critical optical and visual information to judge the health state of bridges. The bridge crack detection methods based on artificial intelligence are essential in this field, but the current approaches are not satisfactory in terms of speed and accuracy. This study proposes a novel multi-scale crack detection network, called MSCNet, comprising a texture enhancement mechanism and feature aggregation to enhance the visual saliency of the objects in the background for bridge crack detection. We use Res2Net as the backbone network to improve the depth information expression ability of the cracks itself. Because the edge property of bridge cracks is prominent, to make full use of this visual feature, we use a texture enhancement module based on group attention to capturing the detailed information of cracks in low-level features. To further mine the depth information of the network, we use a cascade fusion module to capture crack location information in high-level features. Finally, to fully utilize the characteristic information of the deep network, we fuse the low- and high-level features to obtain the final crack prediction. We evaluate the proposed method compared with other state-of-the-art methods on a large-scale crack dataset. The experimental results demonstrate the effectiveness and superiority of the proposed method, which achieves a precision of 93.5%, recall of 94.2%, and inference speed of over 63 FPS.

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.

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.

A Bloom Filter (BF) is a data structure based on probability to compactly represent/record a set of elements (keys). It has wide applications on efficiently identifying a key that has been seen before with minimum amount of recording space used. BF is heavily used in chunking based data de-duplication. Traditionally, a BF is implemented as in-RAM data structure; hence its size is limited by the available RAM space on the machine. For certain applications like data de-duplication that require a big BF beyond the size of available RAM space, it becomes necessary to store a BF into a secondary storage device. Since BF operations are inherently random in nature, magnetic disk provides worse performance for the random read and write operations. It will not be a good fit for storing the large BF. Flash memory based Solid State Drive (SSD) has been considered as an emerging storage device that has superior performance and can potentially replace disks as the preferred secondary storage devices. However, several special characteristics of flash memory make designing a flash memory based BF very challenging. In this paper, our goal is to design an efficient flash memory based BF that is fully aware of these physical characteristics. To this end, we propose a Forest-structured BF design (FBF). FBF uses a combination of RAM and flash memory to design a BF. BF is stored on the flash, while RAM helps to mitigate the impact of slow write performance of flash memory. In addition, in-flash BF is organized in a forest-like structure in order to improve the lookup performance. Our experimental results show that FBF design achieves 2 times faster processing speed with 50% less number of flash write operations when compared with the existing flash memory based BF designs.