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Marco et al. Song et al. In the coding stage, the full-convolution neural network is used to extract defect features, and an attention mechanism is integrated to accelerate the convergence of the model.

As the mainstream target detection framework, Faster R-CNN is used in surface defect detection of steel and metal widely. Wang et al. Dai et al. The research of automatic detection of steel surface defects is focused on in this paper. At the first, the ResNet [ 21 — 24 ] network is reconstructed by deformable convolution as the prefeature extraction network of Faster R-CNN [ 25 — 27 ].

Then, the fixed convolution layer and pooling layer are replaced with deformable convolution layer and deformable pooling layer. Finally, the FPN [ 28 — 30 ] network is used for multifeature fusion, and the soft nonmaximum suppression soft NMS [ 31 ] is used to reduce the confidence of the detection frame larger than the threshold, so as to alleviate the situation of the target missing detection.

According to the test results on the open dataset NEU-DET, the proposed algorithm can effectively detect a variety of defects on steel surface, which is higher than the ordinary steel surface detection algorithms in accuracy.

Faster R-CNN is the mainstream two-stage target detection algorithm, which is used in face detection and defect detection widely. Faster R-CNN algorithm structure includes feature extraction network, region proposal network RPN , and regional convolution neural network mainly. The algorithm structure is shown in Figure 2.

Firstly, the features of the input image are extracted by the feature extraction network; then the extracted features are shared to the RPN network and R-CNN network. Next, the region of interest ROI of the image is extracted. Finally, the detection results are output through ROI pooling and fully connected layer.

In this paper, a steel defect detection algorithm based on the deformable network [ 35 ] and multiscale feature fusion is proposed. Faster R-CNN is used as the basic framework, which is composed of feature extraction network, regional recommendation network, and detection network.

The improved method is shown in Figure 3 , in which ResNet is used as a feature extraction network. Firstly, the deformable convolution is used to reconstruct the ResNet network. Secondly, the feature pyramid network FPN is used to fuse the multiscale features, and the fixed region of interest ROI pooling layer is replaced by the variable pooling layer.

Finally, the soft nonmaximum value suppression algorithm is used to suppress the detection frame with obvious overlap with the highest score detection frame. The conventional convolution network or fully connected network will lead to the loss of partial information in information transmission, which can not train a deep network. However, the ResNet transmits the input information to the output directly, which can protect the integrity of information and reduce the learning difficulty.

Therefore, ResNet is selected as the feature extraction network in this paper. A bottleneck module is used in ResNet, as shown in Figure 4. Aiming at the small and complex defects on the steel surface, convolution layers of stages 2 and stage 4 as shown in Figure 5 are reconstructed by deformable convolution to improve the ability to extract features.

The block convolution kernels of fixed size are often used in the conventional convolution neural network while building model transformation, which is usually limited to fixed geometry structure.

The convolution unit is weak in feature extraction of convolution layer with fixed sampling points, which makes the network difficult to adapt to geometric transformation. To solve the above problems, Dai et al. The end-to-end training is carried out through standard backpropagation to generate a deformable convolution network.

There is no fixed geometric structure due to the different shapes of steel surface defects. Therefore, the idea of deformable convolution is introduced to reconstruct ResNet for the poor adaptability to the ability of unknown changes and weak generalization, so as to improve the recognition ability of the neural network for irregular targets. The offset variable is added to each element of the convolution kernel in deformable convolution, which is calculated by standard convolution unit.

Therefore, the range in the training process can be expanded by the convolution kernel. In addition, the size and position can be adjusted dynamically to adapt to the geometric deformation of different objects in accordance with the information of the image recognized. A convolution layer is added to the input feature map extracted by the conventional convolution kernel to obtain the deformation offset of deformable convolution.

The convolution kernel is used to generate feature map and offset to realize synchronous learning. The various forms of the conventional convolution kernel and the deformable convolution kernel are shown in Figure 6 , where Figure 6 a represents the conventional convolution kernel sampling point, Figure 6 b represents the deformable convolution kernel sampling point after adding offset variables, Figures 6 c and 6 d are special cases of deformable convolution kernel sampling.

In the conventional convolution kernel, the convolution of each pixel in the input image at position of is expressed as follows:. An offset variable is introduced into the deformable convolution; the deformation convolution of each pixel in the input image is expressed as follows:.

In formulas 1 and 2 [ 36 ], is the output feature map, is the input feature map, is the position of pixel, is the parameter of weight, is any pixel in convolution, and is the offset value. The bias domain of deformable convolution points to the sampling point with a strong purpose and outputs more feature information because the bias matrix makes the sampling position of convolution transform freely, so when the effect of deformable convolution is stacked, the feature extraction ability is greatly improved.

The sampling position of the conventional convolution on the target is fixed, as shown in Figure 7 a , while the receptive field can be learned adaptively by the deformable convolution during calculation in Figure 7 b , which has a strong adaptive extraction ability for complex and irregular targets.

It can be adjusted adaptively under the shape and size of the target. Therefore, this adaptive learning receptive field is very necessary in steel defect detection. ROI pooling module is introduced to maximize the pool of the proposed regions after segmentation in order to map the input regions of different sizes into feature vectors of the same length and output a fixed size feature map. The fixed ROI pooling module is replaced with the deformable ROI pooling module in the proposed method in order to enhance the modeling ability of the network for geometric transformation and increase the pool area of defects to obtain the location accurately of complex defects on the steel surface.

The network structure of deformable ROI pooling is shown in Figure 8. The regions of interest are divided into blocks of by ROI pooling, and the size of each area is. The output of normal ROI pooling [ 36 ] is as follows:. As can be seen from Figure 7 , in the deformable ROI pooling, ROI pooling generates convolution feature graphs and then generates regularized offsets with the size of at each position through the full-connection layer.

Finally, the region with enhanced offset is pooled to generate the output feature map. Referring to the idea of deformable convolution, the expression of deformable ROI pooling [ 36 ] is as follows: where is the output characteristic graph after pooling, is the input feature map, is the upper left corner pixel of ROI, is a pixel at any position, is the set of horizontal and vertical coordinates of the pixel, is the pixel value of the grid, and is the offset at each location, where.

The features of the last layer are used in RPN because the top-level features of the network have the strongest semantic information in Faster R-CNN, but this idea is not suitable for small target detection. The feature pyramid network FPN is introduced into Faster R-CNN to perform degree scale fusion operation on the feature map in order to improve the ability of the network to detect small area defects on the steel surface.

The structure is shown in Figure 9. The P6 is obtained by downsampling with the two largest pools on P5, and the multiscale fusion feature combination is output. The target candidate frame of interest is generated by using the fused features in RPN, and the detection results are obtained by classification.

Nonmaximum suppression NMS is used as a postprocessing method commonly to eliminate duplicate frames and reduce the false detection rate in the target detection. However, other similar targets attached to the box with higher prediction score are removed by the NMS, which will lead to missed detection and false detection of similar targets with close proximity and overlapping.

The reduction range of the prediction accuracy of dense targets nearby is determined by soft NMS in light of the intersection over union IOU [ 36 ] of the highest score box, so as to improve the prediction accuracy of dense targets.

For example, the IOU of the detection frames and [ 35 ] is the following: where , are the areas of the detection frame and , respectively, and is the overlapping area of the detection frames and.

The expressions of traditional NMS and Soft NMS algorithms are as follows [ 35 ]: where is the score of the detection box , is the maximum score box, is the collection of detection frames , is the threshold set.

The NMS sets the score of the detection frame whose IOU is greater than the threshold value to 0, while the soft NMS attenuates the score of the detection frame whose IOU is greater than the threshold value, which can alleviate the problems of missed detection and false detection of targets.

The experimental platform is shown in Table 1 and the initialization model parameters of the experiment are shown in Table 2. The average precision AP is used as the evaluation index of each defect detection in this experiment after the model training is completed, and the mean average precision mAP is used as the evaluation index of the whole model performance.

The definitions of precision P , AP, and mAP [ 37 ] are shown as follows: where is the number of samples with correct detection, is the number of negative samples with detection, and is the value of recall R.

The definition of recall rate is shown in formula 10 , can be expressed as formula 11 [ 37 ], is the category index, is the total number of categories, and is the average precision of each category: where is the number of samples with error detection and is the recall rate of. The results and discussion may be presented separately, or in one combined section, and may optionally be divided into headed subsections.

The proposed algorithm is verified on the open dataset of steel defects, and the experimental results are shown in Table 3. There are pictures in total. Among them, there are pictures of six kinds of defects. The proposed algorithm still has the possibility of optimizing the detection of small targets due to the fuzzy and small size of crazing defects and a small number of missed detection.

It can be seen from Figures 10 a — 10 c that the defect target can also be detected in the case of light or dark, which indicates that the algorithm proposed in this paper is also suitable for defect detection on steel surface under complex background.

The proposed scheme is compared with different improved schemes on the same dataset in order to verify the effectiveness of it. The specific improvement schemes are shown in Table 4. The final test results of the four schemes are shown in Figure The experimental results show that Schemes 1, 2, and 3 have missed detection for six kinds of defects, and the detection accuracy is low.

Therefore, the nonmaximum score detection frame is less suppressed compared with Schemes 1—3, and the detection of crazing defects is more accurate. The specific test results are shown in Table 5. After replacing the original feature extraction network with ResNet, the average overall accuracy of Scheme 2 is 1.

The mean average accuracy of Scheme 4 is The mean average accuracy is increased by It can be seen that the idea of fusing multiscale features with FPN has a certain positive impact on the network from the data of Scheme 4, and the effect is better than that of the model obviously with only deformable network. The scheme of replacing NMS with soft NMS reduces the average accuracy of crazing defects but improves the average accuracy on the whole.

It also has a certain attenuation on the suppression of nonhighest detection frame and reduces the situation that defects with the large overlapping area are missed.

Therefore, the combination of the deformable network and multiscale feature fusion scheme can improve the detection performance of the model. The proposed algorithm is compared with other mainstream target detection algorithms in order to further verify the advantages of the proposed algorithm, as shown in Table 6.

The mean average accuracy of the proposed algorithm is It shows that the proposed algorithm has a certain breakthrough in steel surface defect detection. Section further provided the specific terms, actions, limitations, exclusions, and definitions of activities to be included in the CX established. As directed by this section, the BLM was to establish the CX that meets these same specific terms, actions, limitations, exclusions, and definitions; and to establish the CX within one year of the enactment of the legislation by December 20, The attached text shall provide the text for BLM use of this new CX until the revised handbook is published.

Updating the NEPA Handbook to include this CX will assist with its use and meet the direction for management of mule deer and sage-grouse habitat. This IM adds Appendix 12, pending publication of the full revision. Signed by: Authenticated by: Brian St. Virtual reality is similarly being studied as a way to enhance exposure therapy for a number of disorders, including post-traumatic stress disorder and anxiety disorders.

This orientation emphasizes that the overarching goal of exposure should be distress tolerance rather than fear reduction. Accordingly, the authors outline ideas for how to translate this approach into clinical practice with exposure therapy, such as maximizing the extent to which people's expectancies of feared outcomes are violated, pairing a previously extinguished cue with a new conditioned stimulus, removing safety signals, and practicing exposures in multiple contexts e.

By homing in on processes thought to underlie mechanisms of change, it may be possible to maximize the benefits of extinction learning, thus leading to greater improvement in psychopathological symptoms.

Finally, some studies have investigated the advantage of enhancing ERP with medication implicated in facilitated extinction learning. Specifically, research demonstrated that, relative to those given a placebo pill, patients taking d-cycloserine before engaging in exposure therapy experienced a faster rate of symptom improvement in the first few weeks of receiving ERP.

Investigations on the biological underpinnings of OCD have identified genetic factors and abnormalities in neurocircuitry that are associated with the disorder.

Notable exceptions are recent studies that have identified gene variants of brain-derived neurotrophic factor BDNF and fatty acid amide hydrolase FAAH that mediate outcome to psychotherapeutic treatment. The BDNF gene codes for a protein that promotes neuron development and growth and helps to regulate the neurophysiological response to stress, making it especially relevant to better understanding mood and anxiety disorders. FAAH, on the other hand, is a gene in the endocannabinoid system, which plays an important role in regulating anxiety and facilitating fear extinction, which is central to ERP as noted above.

This finding suggests that it may be possible to identify individuals who are more responsive to treatments that entail extinction learning. However, a more recent study in children with anxiety disorders found only minimal evidence of a correlation between gene variants in the endocannabinoid system and response to CBT.

Finally, several surgical and noninvasive neurological interventions are available to patients who have not had success with psychotherapy or medication.

Neuromodulating methods such as transcranial direct-current stimulation tDCS and transcranial magnetic stimulation TMS , as well as surgical procedures such as deep brain stimulation, work to decrease symptoms by targeting underlying neurocircuitry implicated in the pathophysiology of OCD.

However, there are encouraging reports that indicate some benefit of combining tDCS with CBT for treatment-resistant depression,[ 69 ] suggesting it might likewise be useful as an augmentation for the treatment of other disorders. Finally, in their meta-analysis, Berlim et al.

None of the studies included in the meta-analysis examined TMS in combination with ERP; hence, whether or not they would be beneficial when used together merits further study.

Morphometric studies have revealed that the thickness[ 74 ] and volume[ 75 ] of different brain regions in individuals with OCD are correlated with treatment outcomes with exposure therapy. What remains to be seen, however, is if variation in neurocircuitry, such as genetic variants, can ultimately predict differential response to treatment and whether brain imaging findings at baseline can be usefully applied to individual patients. Since OCD is caused by a complex interaction among genetic, neurocircuitry, environmental, and developmental factors, it is essential that researchers continue to integrate psychological and biological approaches to more effectively treat this debilitating disease.

Although ERP has been identified as a nonpharmacological gold standard treatment for OCD, other psychotherapeutic treatments have been developed and their efficacy empirically supported see Manjula and Sudhir review in this issue for more details. Two that have been found to be effective in treating OCD include cognitive therapy and acceptance and commitment therapy ACT.

Despite the fact that ERP, cognitive therapy, and ACT are considered distinct treatments grounded in different theoretical perspectives, they share common elements that perhaps make them more similar than they seem on the surface.

Although there are a number of explanations for its mechanism of action, it is still unclear exactly how it works or why some people respond to it whereas others do not.

These shortcomings underscore the need to continue to improve upon ERP by enhancing it with new methods, incorporating genetic and neurobiological approaches, and developing alternative treatments.

National Center for Biotechnology Information , U. Search database Search term. Journal List Indian J Psychiatry v. Indian J Psychiatry. Dianne M. Hezel 1, 2 and H. Blair Simpson 1, 2. Blair Simpson. Author information Copyright and License information Disclaimer. Address for correspondence: Dr. E-mail: ude. This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.

This article has been cited by other articles in PMC. Abstract Obsessive-compulsive disorder OCD is characterized by distressing thoughts and repetitive behaviors that are interfering, time-consuming, and difficult to control. Keywords: Cognitive-behavioral therapy, exposure with response prevention, obsessive-compulsive disorder, treatment efficacy. Open in a separate window. Figure 1. Access to care There are numerous barriers to treatment, including high costs of care, stigma surrounding mental health issues, and lack of access to clinicians who are trained in evidence-based practices.

Enhancing exposure and response prevention Technological advances have been used not only to disseminate ERP but also in an attempt to improve its effects.

Integrating biological and psychological approaches Investigations on the biological underpinnings of OCD have identified genetic factors and abnormalities in neurocircuitry that are associated with the disorder.

Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest. The global burden of anxiety disorders in Psychol Med. Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization world mental health surveys.

Association AP. Meyer V. Modification of expectations in cases with obsessional rituals. Behav Res Ther. Foa EB, Goldstein A. Continuous exposure and complete response prevention in the treatment of obsessive-compulsive neurosis.

Behav Ther. Annu Rev Clin Psychol. Treatment of chronic obsessive-compulsive neurosis by in-vivo exposure. A two-year follow-up and issues in treatment.

Br J Psychiatry. Mowrer OH. A stimulus-response analysis of anxiety and its role as a reinforcing agent. Psychol Rev. Abramowitz JS. Effectiveness of psychological and pharmacological treatments for obsessive-compulsive disorder: A quantitative review.

J Consult Clin Psychol. The psychological treatment of obsessive-compulsive disorder. Can J Psychiatry. A comparison of behavioral group therapy and individual behavior therapy in treating obsessive-compulsive disorder. J Nerv Ment Dis. Cognitive-behavioral treatment of obsessive thoughts: A controlled study. Controlled trial of exposure and response prevention in obsessive-compulsive disorder.

Psychotherapy for obsessive-compulsive disorder. Curr Psychiatry Rep. A multidimensional meta-analysis of psychotherapy and pharmacotherapy for obsessive-compulsive disorder. Clin Psychol Rev. Efficacy of cognitive behavioral therapy for anxiety disorders: A review of meta-analytic findings. Psychiatr Clin North Am. Effectiveness of exposure and ritual prevention for obsessive-compulsive disorder: Randomized compared with nonrandomized samples.

Changes in quality of life following cognitive-behavioral therapy for obsessive-compulsive disorder. Sleep-related problems in pediatric obsessive-compulsive disorder. J Anxiety Disord. Context in the clinic: How well do cognitive-behavioral therapies and medications work in combination?