Inspection Robots in Oil and Gas Industry: a Review of Current Solutions and Future Trends <s> C. Inspection Technologies <s> We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds. <s> BIB001 </s> Inspection Robots in Oil and Gas Industry: a Review of Current Solutions and Future Trends <s> C. Inspection Technologies <s> The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade. <s> BIB002 </s> Inspection Robots in Oil and Gas Industry: a Review of Current Solutions and Future Trends <s> C. Inspection Technologies <s> Accurate detection of rivers plays a significant role in water conservancy construction and ecological protection, where airborne synthetic aperture radar (SAR) data have already become one of the main sources. However, extracting river information from radar data efficiently and accurately still remains an open problem. The existing methods for detecting rivers are typically based on rivers’ edges, which are easily mixed with those of artificial buildings or farmland. In addition, pixel-based image processing approaches cannot meet the requirement of real-time processing. Inspired by the feature integration and target recognition capabilities of biological vision systems, in this paper, we present a hierarchical method for automated detection of river networks in the high-resolution SAR data using biologically visual saliency modeling. For effective saliency detection, the original image is first over-segmented into a set of primitive superpixels. A visual feature set is designed to extract a regional feature histogram, which is then quantized based on the optimal parameters learned from the labeled SAR images. Afterward, three saliency measurements based on the specificity of the rivers in the SAR images are proposed to generate a single layer saliency map, i.e., local region contrast, boundary connectivity, and edge density. Finally, by exploiting belief propagation, we propose a multi-layer saliency fusion approach to derive a high-quality saliency map. Extensive experimental results on three airborne SAR image data sets with the ground truth demonstrate that the proposed saliency model consistently outperforms the existing saliency target detection models. <s> BIB003 </s> Inspection Robots in Oil and Gas Industry: a Review of Current Solutions and Future Trends <s> C. Inspection Technologies <s> Synthetic aperture radar (SAR) automatic target recognition (ATR) technology is one of the research hotspots in the field of image cognitive learning. Inspired by the human cognitive process, experts have designed convolutional neural network (CNN)-based SAR ATR methods. However, the performance of CNN significantly deteriorates when the labeled samples are insufficient. To effectively utilize the unlabeled samples, we present a novel semi-supervised CNN method. In the training process of our method, the information contained in the unlabeled samples is integrated into the loss function of CNN. Specifically, we first utilize CNN to obtain the class probabilities of the unlabeled samples. Thresholding processing is performed to optimize the class probabilities so that the reliability of the unlabeled samples is improved. Afterward, the optimized class probabilities are used to calculate the scatter matrices of the linear discriminant analysis (LDA) method. Finally, the loss function of CNN is modified by the scatter matrices. We choose ten types of targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The experimental results show that the recognition accuracy of our method is significantly higher than other semi-supervised methods. It has been proved that our method can effectively improve the SAR ATR accuracy when labeled samples are insufficient. <s> BIB004 </s> Inspection Robots in Oil and Gas Industry: a Review of Current Solutions and Future Trends <s> C. Inspection Technologies <s> Abstract Defect inspection is extremely crucial to ensure the quality of steel surface. It affects not only the subsequent production, but also the quality of the end-products. However, due to the rare occurrence and appearance variations of defects, surface defect identification of steels has always been a challenging task. Recently, deep learning methods have shown outstanding performance in image classification, especially when there are enough training samples. Since most sample images of steel surface are unlabeled, a new semi-supervised learning method is proposed to classify surface defects of steels. The new method is named CAE-SGAN, as it is based on Convolutional Autoencoder (CAE) and semi-supervised Generative Adversarial Networks (SGAN). CAE-SGAN first trains a stacked CAE through massive unlabeled data. Considering the appearance variations of defects, the passthrough layer is used to help CAE extract fine-grained features. After CAE is trained, the encoder network of CAE is reserved as the feature extractor and fed into a softmax layer to form a new classifier. SGAN is introduced for semi-supervised learning to further improve the generalization ability of the new method. The classifier is trained with images collected from real production lines and images randomly generated by SGAN. Extensive experiments are carried out with samples captured from different steel production lines, and the results indicate that CAE-SGAN had yielded best performances compared with traditional methods. Especially for hot rolled plates, the classification rate is improved by around 16%. <s> BIB005 </s> Inspection Robots in Oil and Gas Industry: a Review of Current Solutions and Future Trends <s> C. Inspection Technologies <s> Abstract Crack detection is a critical task in monitoring and inspection of civil engineering structures. Image classification and bounding box approaches have been proposed in existing vision-based automated concrete crack detection methods using deep convolutional neural networks. The current study proposes a crack detection method based on deep fully convolutional network (FCN) for semantic segmentation on concrete crack images. Performance of three different pre-trained network architectures, which serves as the FCN encoder's backbone, is evaluated for image classification on a public concrete crack dataset of 40,000 227 × 227 pixel images. Subsequently, the whole encoder-decoder FCN network with the VGG16-based encoder is trained end-to-end on a subset of 500 annotated 227 × 227-pixel crack-labeled images for semantic segmentation. The FCN network achieves about 90% in average precision. Images extracted from a video of a cyclic loading test on a concrete specimen are used to validate the proposed method for concrete crack detection. It was found that cracks are reasonably detected and crack density is also accurately evaluated. <s> BIB006
It can be clearly seen from the review that one experienced engineer is needed to monitor the image sequences captured by the UAVs and AUVs. What is more, to realise a fully autonomous inspection system, autonomous detecting corrosions and cracks is essential. With the development of image recognition, especially with the deep learning algorithms, it is possible to inspect the assets in oil and gas industries autonomously. Deep learning methods, especially convolutional neural networks, which has made many breakthroughs in computer vision tasks BIB003 [43] BIB004 . It can extract features autonomously for all kinds of objects, which is more accurate and robust than traditional man designed features, such as SIFT BIB002 and HOG BIB001 . It has led to great improvements in many vision inspection tasks. Beijing University of Science and Technology has adopted the deep learning methods to detect steel surface, which is vital to the steel product. With the help of deep learning method, the detection precision of hot rolled plates has been improved around 16% than traditional methods BIB005 . In  , a deep learning algorithm was used to detect breaches, dents, burrs and abrasions on the sealing surface of a container. The accuracy and efficiency of this method has been proved by the real-world test in the filling line. The autonomous detection of concrete structure cracks can also be realised by deep learning. Dung C. V. developed a deep learning based autonomous concrete crack detection algorithm, which can reach approximately 90% of the maximum Average precision (AP) and F-Score (F1) on validation and test datasets BIB006 . With so many successful works in inspection tasks with deep learning, it is possible to develop autonomous corrosion and cracks detection approaches in the future. As a result, the inspection quality, speed, as well as the overall inspection process will be improved.