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Currently, object detection on remote sensing images has drawn significant attention due to its extensive applications, including environmental monitoring, urban planning, and disaster assessment. However, detecting objects in the aerial images captured by remote sensors presents unique challenges compared to natural images, such as low resolution, complex backgrounds, and variations in scale and angle. Prior object detection algorithms are limited in their ability to identify oriented small objects, especially in aerial images where small objects are usually obscured by background noise. To address the above limitations, a novel framework (ORCNN-X) was proposed for oriented small object detection in remote sensing images by improving the Oriented RCNN. The framework adopts a multiscale feature extraction network (ResNeSt+) with a dynamic attention module (DCSA) and an effective feature fusion mechanism (W-PAFPN) to enhance the model's perception ability and handle variations in scale and angle. The proposed framework is evaluated based on two public benchmark datasets, DOTA and HRSC2016. The experiments demonstrate its state-of-the-art performance in aspects of detection accuracy and speed. The presented model can also represent more objective spatial location information according to the feature visualization maps. Specifically, our model outperforms the baseline model by 1.43% mAP50 and 1.37% mAP12 on DOTA and HRSC2016 datasets, respectively. [ABSTRACT FROM AUTHOR]

During oil-well production, there are often cracks, breaks, and perforation corrosion on the screen pipe that can significantly deteriorate sand control and pipe strength. To repair damaged screen pipes, we developed a technique originating from the tube hydroforming, and the feasibility of the technique was systematically investigated. First, the elastoplastic mechanics of patch tubes during the hydroforming process was analyzed to investigate the forming mechanism. Second, tensile experiments showed that AISI 321 after cold drawn and solution had good mechanical properties. A numerical simulation model of a hydroforming patch composed of AISI 321 steel was built to investigate the effect of structural parameters such as the length, initial outer diameter, and thickness of a patch tube on hydroforming patch performance. Forming pressure did not significantly change with length, but it decreased with initial outer diameter and increased with thickness. In addition to the simulation, a hydroforming test bench was constructed to experimentally test the patch method. Test results showed that the patch tube could fit closely with the screen base pipe, and residual contact stress could be more than 139.78 kN/m2. Deformation strengthening due to the deformed martensite was conducive to improving the strength of the patch tube after hydroforming. The combination of the simulation and experiment indicates that the application of hydroforming patch technology can effectively repair damaged screen pipes. [ABSTRACT FROM AUTHOR]

Due to the advantages of economics, safety, and efficiency, vision-based analysis techniques have recently gained conspicuous advancements, enabling them to be extensively applied for autonomous constructions. Although numerous studies regarding the defect inspection and condition assessment in underground sewer pipelines have presently emerged, we still lack a thorough and comprehensive survey of the latest developments. This survey presents a systematical taxonomy of diverse sewer inspection algorithms, which are sorted into three categories that include defect classification, defect detection, and defect segmentation. After reviewing the related sewer defect inspection studies for the past 22 years, the main research trends are organized and discussed in detail according to the proposed technical taxonomy. In addition, different datasets and the evaluation metrics used in the cited literature are described and explained. Furthermore, the performances of the state-of-the-art methods are reported from the aspects of processing accuracy and speed. [ABSTRACT FROM AUTHOR]

With the rapid rise of private vehicles around the world, License Plate Recognition (LPR) plays a vital role in supporting the government to manage vehicles effectively. However, an introduction of new types of license plate (LP) or slight changes in the LP format can break previous LPR systems, as they fail to recognize the LP. Moreover, the LPR system is extremely sensitive to the conditions of the surrounding environment. Thus, this paper introduces a novel deep learning-based Korean LPR system that can effectively deal with existing challenges. The main contributions of this study include (1) a robust LPR system with the integration of three pre-processing techniques (defogging, low-light enhancement, and super-resolution) that can effectively recognize the LP under various conditions, (2) the establishment of two original Korean LPR approaches for different scenarios, including whole license plate recognition (W-LPR) and single-character license plate recognition (SC-LPR), and (3) the introduction of two Korean LPR datasets (synthetic data and real data) involving a new type of LP introduced by the Korean government. Through several experiments, the proposed LPR framework achieved the highest recognition accuracy of 98.94%. [ABSTRACT FROM AUTHOR]

The interface passivation of the HfO2/Si0.7Ge0.3 stack is systematically investigated based on low-temperature ozone oxidation and Si-cap methods. Compared with the Al2O3/Si0.7Ge0.3 stack, the dispersive feature and interface state density (Dit) of the HfO2/Si0.7Ge0.3 stack MOS (Metal-Oxide-Semiconductor) capacitor under ozone direct oxidation (pre-O sample) increases obviously. This is because the tiny amounts of GeOx in the formed interlayer (IL) oxide layer are more likely to diffuse into HfO2 and cause the HfO2/Si0.7Ge0.3 interface to deteriorate. Moreover, a post-HfO2-deposition (post-O) ozone indirect oxidation is proposed for the HfO2/Si0.7Ge0.3 stack; it is found that compared with pre-O sample, the Dit of the post-O sample decreases by about 50% due to less GeOx available in the IL layer. This is because the amount of oxygen atoms reaching the interface of HfO2/Si0.7Ge0.3 decreases and the thickness of IL in the post-O sample also decreases. To further reduce the Dit of the HfO2/Si0.7Ge0.3 interface, a Si-cap passivation with the optimal thickness of 1 nm is developed and an excellent HfO2/Si0.7Ge0.3 interface with Dit of 1.53 × 1011 eV−1cm−2 @ E−Ev = 0.36 eV is attained. After detailed analysis of the chemical structure of the HfO2/IL/Si-cap/Si0.7Ge0.3 using X-ray photoelectron spectroscopy (XPS), it is confirmed that the excellent HfO2/Si0.7Ge0.3 interface is realized by preventing the formation of Hf-silicate/Hf-germanate and Si oxide originating from the reaction between HfO2 and Si0.7Ge0.3 substrate. [ABSTRACT FROM AUTHOR]

The radish is a delicious, healthy vegetable and an important ingredient to many side dishes and main recipes. However, climate change, pollinator decline, and especially Fusarium wilt cause a significant reduction in the cultivation area and the quality of the radish yield. Previous studies on plant disease identification have relied heavily on extracting features manually from images, which is time-consuming and inefficient. In addition to Red-Green-Blue (RGB) images, the development of near-infrared (NIR) sensors has enabled a more effective way to monitor the diseases and evaluate plant health based on multispectral imagery. Thus, this study compares two distinct approaches in detecting radish wilt using RGB images and NIR images taken by unmanned aerial vehicles (UAV). The main research contributions include (1) a high-resolution RGB and NIR radish field dataset captured by drone from low to high altitudes, which can serve several research purposes; (2) implementation of a superpixel segmentation method to segment captured radish field images into separated segments; (3) a customized deep learning-based radish identification framework for the extracted segmented images, which achieved remarkable performance in terms of accuracy and robustness with the highest accuracy of 96%; (4) the proposal for a disease severity analysis that can detect different stages of the wilt disease; (5) showing that the approach based on NIR images is more straightforward and effective in detecting wilt disease than the learning approach based on the RGB dataset. [ABSTRACT FROM AUTHOR]

Unreinforced masonry walls are extensively used in the petrochemical industry and they are one of the most vulnerable components to blast loads. To investigate the failure modes and improve the blast resistances of masonry walls, four full-scale field tests were conducted using unreinforced and spray-on polyurea-reinforced masonry walls subjected to gas explosions. The results suggested that the primary damage of the unreinforced masonry wall was flexural deformation and the wall collapsed at the latter stage of gas explosion. The presence of polyurea coatings could effectively improve the anti-explosion abilities of masonry walls, prevent wall collapses, and retain the flying fragments, which would reduce the casualties and economic losses caused by petrochemical explosion accidents. The bond between the polymer and masonry wall was critical, and premature debonding resulted in a failure of the coating to exert the maximum energy absorption effect. A numerical model for masonry walls was developed in ANSYS/LS-Dyna and validated with the test data. Parametric studies were conducted to explore the influences of the polyurea-coating thickness and spray pattern on the performances of masonry walls. The polyurea-coating thickness and spray pattern affected the resistance capacities of masonry walls significantly. [ABSTRACT FROM AUTHOR]