Bridge crack detection based on improved single shot multi-box detector.

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.

Semantic segmentation method of underwater images based on encoder-decoder architecture.

With the exploration and development of marine resources, deep learning is more and more widely used in underwater image processing. However, the quality of the original underwater images is so low that traditional semantic segmentation methods obtain poor segmentation results, such as blurred target edges, insufficient segmentation accuracy, and poor regional boundary segmentation effects. To solve these problems, this paper proposes a semantic segmentation method for underwater images. Firstly, the image enhancement based on multi-spatial transformation is performed to improve the quality of the original images, which is not common in other advanced semantic segmentation methods. Then, the densely connected hybrid atrous convolution effectively expands the receptive field and slows down the speed of resolution reduction. Next, the cascaded atrous convolutional spatial pyramid pooling module integrates boundary features of different scales to enrich target details. Finally, the context information aggregation decoder fuses the features of the shallow network and the deep network to extract rich contextual information, which greatly reduces information loss. The proposed method was evaluated on RUIE, HabCam UID, and UIEBD. Compared with the state-of-the-art semantic segmentation algorithms, the proposed method has advantages in segmentation integrity, location accuracy, boundary clarity, and detail in subjective perception. On the objective data, the proposed method achieves the highest MIOU of 68.3 and OA of 79.4, and it has a low resource consumption. Besides, the ablation experiment also verifies the effectiveness of our method.

Adversarial Reinforcement Learning With Object-Scene Relational Graph for Video Captioning.

Existing video captioning methods usually ignore the important fine-grained semantic attributes, the video diversity, as well as the association and motion state between objects within and between frames. Thus, they cannot adapt to small sample data sets. To solve the above problems, this paper proposes a novel video captioning model and an adversarial reinforcement learning strategy. Firstly, an object-scene relational graph model is designed based on the object detector and scene segmenter to express the association features. The graph is encoded by the graph neural network to enrich the expression of visual features. Meanwhile, a trajectory-based feature representation model is designed to replace the previous data-driven method to extract motion and attribute information, so as to analyze the object motion in the time domain and establish the connection between the visual content and language under small data sets. Finally, an adversarial reinforcement learning strategy and a multi- branch discriminator are designed to learn the relationship between the visual content and corresponding words so that rich language knowledge is integrated into the model. Experimental results on three standard datasets and one small sample dataset indicate that our proposed method achieves state-of-the-art performance. Also, the ablation experiments and visualization results verify the effectiveness of proposed each strategy.

Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information.

Deep learning is currently the mainstream method of object detection. Faster region-based convolutional neural network (Faster R-CNN) has a pivotal position in deep learning. It has impressive detection effects in ordinary scenes. However, under special conditions, there can still be unsatisfactory detection performance, such as the object having problems like occlusion, deformation, or small size. This paper proposes a novel and improved algorithm based on the Faster R-CNN framework combined with the Faster R-CNN algorithm with skip pooling and fusion of contextual information. This algorithm can improve the detection performance under special conditions on the basis of Faster R-CNN. The improvement mainly has three parts: The first part adds a context information feature extraction model after the conv5_3 of the convolutional layer; the second part adds skip pooling so that the former can fully obtain the contextual information of the object, especially for situations where the object is occluded and deformed; and the third part replaces the region proposal network (RPN) with a more efficient guided anchor RPN (GA-RPN), which can maintain the recall rate while improving the detection performance. The latter can obtain more detailed information from different feature layers of the deep neural network algorithm, and is especially aimed at scenes with small objects. Compared with Faster R-CNN, you only look once series (such as: YOLOv3), single shot detector (such as: SSD512), and other object detection algorithms, the algorithm proposed in this paper has an average improvement of 6.857% on the mean average precision (mAP) evaluation index while maintaining a certain recall rate. This strongly proves that the proposed method has higher detection rate and detection efficiency in this case.

Spatial-Semantic and Temporal Attention Mechanism-Based Online Multi-Object Tracking.

Multi-object tracking (MOT) plays a crucial role in various platforms. Occlusion and insertion among targets, complex backgrounds and higher real-time requirements increase the difficulty of MOT problems. Most state-of-the-art MOT approaches adopt the tracking-by-detection strategy, which relies on compute-intensive sliding windows or anchoring schemes to detect matching targets or candidates in each frame. In this work, we introduce a more efficient and effective spatial-temporal attention scheme to track multiple objects in various scenarios. Using a semantic-feature-based spatial attention mechanism and a novel Motion Model, we address the insertion and location of candidates. Some online-learned target-specific convolutional neural networks (CNNs) were used to estimate target occlusion and classify by adapting the appearance model. A temporal attention mechanism was adopted to update the online module by balancing current and history frames. Extensive experiments were performed on Karlsruhe Institute of Technologyand Toyota Technological Institute (KITTI) benchmarks and an Armored Target Tracking Dataset (ATTD) built for ground-armored targets. Experimental results show that the proposed method achieved outstanding tracking performance and met the actual application requirements.

Improved Faster R-CNN Traffic Sign Detection Based on a Second Region of Interest and Highly Possible Regions Proposal Network.

Traffic sign detection systems provide important road control information for unmanned driving systems or auxiliary driving. In this paper, the Faster region with a convolutional neural network (R-CNN) for traffic sign detection in real traffic situations has been systematically improved. First, a first step region proposal algorithm based on simplified Gabor wavelets (SGWs) and maximally stable extremal regions (MSERs) is proposed. In this way, the region proposal a priori information is obtained and will be used for improving the Faster R-CNN. This part of our method is named as the highly possible regions proposal network (HP-RPN). Second, in order to solve the problem that the Faster R-CNN cannot effectively detect small targets, a method that combines the features of the third, fourth, and fifth layers of VGG16 to enrich the features of small targets is proposed. Third, the secondary region of interest method to enhance the feature of detection objects and improve the classification capability of the Faster R-CNN is proposed. Finally, a method of merging the German traffic sign detection benchmark (GTSDB) and Chinese traffic sign dataset (CTSD) databases into one larger database to increase the number of database samples is proposed. Experimental results show that our method improves the detection performance, especially for small targets.

Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs.

Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands.

Association between dietary carrot intake and breast cancer: A meta-analysis.

BACKGROUND: We aimed to estimate the association between dietary carrot intake and risk of breast cancer by conducting a meta-analysis of epidemiologic studies. METHODS: Relevant studies were identified by searching databases through September 2017. We included studies that reported risk estimates with 95% confidence intervals for the association between dietary carrot intake and breast cancer risk. Random-effects models were used to calculate the summary risk estimates. Publication bias was estimated using Begg's funnel plot and Egger's regression asymmetry test. RESULTS: A total of 10 articles met the eligibility criteria and were included in the meta-analysis involving 13,747 cases. The combined odds ratios (ORs) of breast cancer for the highest compared with the lowest dietary carrot intake was 0.79 (95% CI: 0.68, 0.90), and a significant heterogeneity was observed. In the subgroup analyses separated by study design, the inverse associations were more pronounced in the case-control studies than in the cohort studies, while the associations did not significantly differ by geographical region, study quality, exposure assessment. Omission of any single study had little effect on the combined risk estimate. CONCLUSION: The overall current literatures suggested that dietary carrot intake was associated with decreased risk of breast cancer.

Formulation and evaluation of PLGA nanoparticles loaded capecitabine for prostate cancer.

The objective of this work is to prepare and evaluate Poly (D, L-Lactide-co-glycolide) (PLGA) Nanoparticles (NPs) of Capecitabine, an anticancer agent loaded by solvent displacement method using stabilizer (poly vinyl alcohol). The prepared NPs were characterized by FT-IR, DSC, drug loading, entrapment efficiency, particle size, surface morphology by Atomic force microscopy (AFM), X-ray diffraction and in-vitro studies. FT-IR and DSC studies indicated that there was no interaction between the drug and polymer. The morphological studies performed by AFM showed uniform and spherical shaped discrete particles without aggregation and smooth in surface morphology with a nano size range of 144 nm. X-ray diffraction was performed to reveal the crystalline nature of the drug after encapsulation. The NPs formed were spherical in shape with zeta potentials (-14.8 mV). In vitro release studies were carried and showed drug release up to 5 days. The drug release followed zero order kinetics and a Fickian transport mechanism. Nanoparticles obtained a high encapsulation efficiency of 88.4% and drug loading of 16.98%. Drug released from Capecitabine loaded PLGA NPs (84.1%) was for 5 days. It is concluded from the present investigation that PLGA NPs of Capecitabine may effectively deliver the drug to the prostate for the treatment of prostate cancer.

[Genetic risks of intracytoplasmic sperm injection for male infertility].

Recently, intracytoplasmic sperm injection (ICSI) has been extremely successful in the treatment of male infertility. However, the consequent transmission of sperm cytogenetic defects and genetic defects to the offspring has aroused considerable concern. Among infertile men, those with severe spermatogenic defects, including oligozoospermia and azoospermia, are mostly the subjects for ICSI. Therefore it is very important to obtain cytogenetic and chromosomal information on these infertile patients and prevent the inheritance of these genetic defects. This review offers an analysis on the genetic defects among infertile men.