At present, with the advance of satellite image processing technology, remote sensing images are becoming more widely used in real scenes. However, due to the limitations of current remote sensing ...imaging technology and the influence of the external environment, the resolution of remote sensing images often struggles to meet application requirements. In order to obtain high-resolution remote sensing images, image super-resolution methods are gradually being applied to the recovery and reconstruction of remote sensing images. The use of image super-resolution methods can overcome the current limitations of remote sensing image acquisition systems and acquisition environments, solving the problems of poor-quality remote sensing images, blurred regions of interest, and the requirement for high-efficiency image reconstruction, a research topic that is of significant relevance to image processing. In recent years, there has been tremendous progress made in image super-resolution methods, driven by the continuous development of deep learning algorithms. In this paper, we provide a comprehensive overview and analysis of deep-learning-based image super-resolution methods. Specifically, we first introduce the research background and details of image super-resolution techniques. Second, we present some important works on remote sensing image super-resolution, such as training and testing datasets, image quality and model performance evaluation methods, model design principles, related applications, etc. Finally, we point out some existing problems and future directions in the field of remote sensing image super-resolution.
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Concrete-filled square steel tube column (CFSSTC) joints are the most important parts of concrete-filled steel tube frame structures. It is of great significance to study the damage of CFSSTC joints ...under the seismic loads. In this paper, embedded piezoceramic transducers are used to monitor the damage of core concrete of CFSSTC joints under cyclic loading and surface-bonded piezoceramic disks are used to monitor the debonding damage of the steel tube and core concrete of two specimens. The damages of the joints under different loading levels and different loading cycles are evaluated by the received signal of the piezoceramic transducers. The experimental results show that the amplitude of the signal attenuates obviously with the appearance of damage in the joints, and the degree of attenuation increases with the development of the damage. The monitoring results from piezoceramic transducers are basically consistent with the hysteresis loops and skeleton curves of the CFSSTC joints during the cyclic loading. The effectiveness of the piezoceramic transducers are verified by the experimental results in structural health monitoring of the CFSSTC joint under cyclic loading.
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Remote sensing image fusion is a fundamental issue in the field of remote sensing. In this paper, we propose a remote sensing image fusion method based on optimal scale morphological convolutional ...neural networks (CNN) using the principle of entropy from information theory. We use an attentional CNN to fuse the optimal cartoon and texture components of the original images to obtain a high-resolution multispectral image. We obtain the cartoon and texture components using sparse decomposition-morphological component analysis (MCA) with an optimal threshold value determined by calculating the information entropy of the fused image. In the sparse decomposition process, the local discrete cosine transform dictionary and the curvelet transform dictionary compose the MCA dictionary. We sparsely decompose the original remote sensing images into a texture component and a cartoon component at an optimal scale using the information entropy to control the dictionary parameter. Experimental results show that the remote sensing image fusion method proposed in this paper can effectively retain the information of the original image, improve the spatial resolution and spectral fidelity, and provide a new idea for image fusion from the perspective of multi-morphological deep learning.
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Mycotoxins, including aflatoxin B1 (AFB1), zearalenone (ZEN) and deoxynivalenol (DON), are common contaminants of moldy feeds. Mycotoxins can cause deleterious effects on the health of chickens and ...can be carried over in poultry food products. This study was conducted to investigate the effects of moldy corn (containing AFB1, ZEN, and DON) on the performance, health, and mycotoxin residues of laying hens. One hundred and eighty 400-day-old laying hens were divided into 4 treatments: basal diet (Control), basal diet containing 20% moldy corn (MC20), 40% moldy corn (MC40) and 60% moldy corn (MC60). At d 20, 40, and 60, the performance, oxidative stress, immune function, metabolism, and mycotoxin residues in eggs were determined. At d 60, mycotoxin residues in muscle and edible viscera were measured. Results showed the average daily feed intake (ADFI) and laying performance of laying hens were decreased with moldy corn treatments. All the moldy corn treatments also induced significant oxidative stress and immunosuppression, reflected by decreased antioxidase activities, contents of cytokines, immunoglobulins, and increased malonaldehyde level. Moreover, the activities of aspartate aminotransferase and alanine transaminase were increased by moldy corn treatments. The lipid metabolism was influenced in laying hens receiving moldy corn, reflected by lowered levels of total protein, high density lipoprotein cholesterol, low density lipoprotein cholesterol, total cholesterol, and increased total triglyceride as well as uric acid. The above impairments were aggravated with the increase of mycotoxin levels. Furthermore, AFB1 and ZEN residues were found in eggs, muscle, and edible viscera with moldy corn treatments, but the residues were below the maximum residue limits. In conclusion, moldy corn impaired the performance, antioxidant capacity, immune function, liver function, and metabolism of laying hens at d 20, 40, and 60. Moldy corn also led to AFB1 residue in eggs at d 20, 40, and 60, and led to both AFB1 and ZEN residues in eggs at days 40 and 60, and in muscle and edible viscera at d 60. The toxic effects and mycotoxin residues were elevated with the increase of moldy corn levels in feed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Surveillance video has been widely used in business, security, search, and other fields. Identifying and locating specific pedestrians in surveillance video has an important application value in ...criminal investigation, search and rescue, etc. However, the requirements for real-time capturing and accuracy are high for these applications. It is essential to build a complete and smooth system to combine pedestrian detection, tracking and re-identification to achieve the goal of maximizing efficiency by balancing real-time capture and accuracy. This paper combined the detector and Re-ID models into a single end-to-end network by introducing a new track branch to YOLOv5 architecture for tracking. For pedestrian detection, we employed the weighted bi-directional feature pyramid network (BiFPN) to enhance the network structure based on the YOLOv5-Lite, which is able to further improve the ability of feature extraction. For tracking, based on Deepsort, this paper enhanced the tracker, which uses the Noise Scale Adaptive (NSA) Kalman filter to track, and adds adaptive noise to strengthen the anti-interference of the tracking model. In addition, the matching strategy is further updated. For pedestrian re-identification, the network structure of Fastreid was modified, which can increase the feature extraction speed of the improved algorithm by leaps and bounds. Using the proposed unified network, the parameters of the entire model can be trained in an end-to-end method with the multi-loss function, which has been demonstrated to be quite valuable in some other recent works. Experimental results demonstrate that pedestrians detection can obtain a 97% mean Average Precision (mAP) and that it can track the pedestrians well with a 98.3% MOTA and a 99.8% MOTP on the MOT16 dataset; furthermore, high pedestrian re-identification performance can be achieved on the VERI-Wild dataset with a 77.3% mAP. The overall framework proposed in this paper has remarkable performance in terms of the precise localization and real-time detection of specific pedestrians across time, regions, and cameras.
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Black and odorous water seriously affects the ecological balance of rivers and the health of people living nearby. Satellite remote sensing technology with its advantages of a large range, long-time ...series, low cost, and high efficiency, has provided a new area for water quality detection. Much archived remote sensing satellite data can be further processed and used as a data source for black and odorous water detection. In this paper, Gaofen-2 remote sensing data with a spatial resolution of 1 m is leveraged as the data source. To enrich the data source in the northern coastal zone of China, we have built a high-quality remote sensing dataset, called the remote sensing images for black and odorous water detection (RSBD) dataset, which is collected from the Gaofen-2 satellite in Yantai, China. In addition, we propose a network with an encoder-decoder discriminant structure for black and odorous water detection. In the network, an augmented attention module is designed to capture a more comprehensive semantic feature representation. Further, the median balancing loss function is adopted to solve the imbalance issues. Experimental results demonstrate that the network is superior to other state-of-the-art semantic segmentation methods on our dataset.
Traffic signs detection and recognition is an essential and challenging task for driverless cars. However, the detection of traffic signs in most scenarios belongs to small target detection, and most ...existing object detection methods show poor performance in these cases, which increases the difficulty of detection. To further improve the accuracy of small object detection for traffic signs, this paper proposed an optimization strategy based on the YOLOv4 network. Firstly, an improved triplet attention mechanism was added to the backbone network. It was combined with optimized weights to make the network focus more on the acquisition of channel and spatial features. Secondly, a bidirectional feature pyramid network (BiFPN) was used in the neck network to enhance feature fusion, which can effectively improve the feature perception field of small objects. The improved model and some state-of-the-art (SOTA) methods were compared on the joint dataset TT100K-COCO. Experimental results show that the enhanced network can achieve 60.4% mAP(Mean Average Precision), surpassing the YOLOv4 by 8% with the same input size. With a larger input size, it can achieve a best performance capability of 66.4% mAP. This work provides a reference for research on obtaining higher accuracy for traffic sign detection in autonomous driving.
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In a recent study by Jay K. Desai et al., (Desai et al., 2023) the authors extensively documented the effects of long-term exposure to 4-nonylphenol neurotoxicity in zebrafish, including oxidative ...stress markers, behavioral changes, and neuropathology results. The results indicate that, although Neurotoxicity of 4-nonylphenol did not cause evident changes in zebrafish brain tissue pathology, it significantly induced oxidative stress reactions in the zebrafish brain and altered their exploratory behaviors in response to light and dark stimuli.However, upon reviewing the results of this study, we have identified several questionable outcomes and errors in image usage, leading to some concerns.
•Propose the misuse of the author's images and confusion about behavioral outcomes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
ABSTRACTSea-Land Segmentation (SLS) of remote sensing images is a meaningful task in the remote sensing and computer vision community. Some tricky situations, such as intraclass heterogeneity due to ...imaging constraints, inherent interclass similarity of sea-land regions and uncertain sea-land boundaries, still are and continues to be the significant challenges in SLS. In this paper, a fuzzy-embedded multi-scale prototype network, named FMPNet, is proposed to target the above challenges of SLS task. We design a dual-branch joint attention feature extraction module (DAFM) for effective feature extraction. Memory bank (MB) is designed to collect multi-scale prototypes, aiming to obtain discriminative feature representations and guide feature selection. In addition, fuzzy connection (FC) unit is embedded in the network structure to mitigate the uncertain sea-land boundaries through 2D Gaussian fuzzy method. Extensive experimental results on a publicly SLS dataset and real region images captured by the Gaofen-1 satellite demonstrate the superior performance of the proposed FMPNet over the other state-of-the-art methods.
Show-through phenomena have always been a challenging issue in color-document image processing, which is widely used in various fields such as finance, education, and administration. Existing methods ...for processing color-document images face challenges, including dealing with double-sided documents with show-through effects, accurately distinguishing between foreground and show-through parts, and addressing the issue of insufficient real image data for supervised training. To overcome these challenges, this paper proposes a self-supervised-learning-based method for removing show-through effects in color-document images. The proposed method utilizes a two-stage-structured show-through-removal network that incorporates a double-cycle consistency loss and a pseudo-similarity loss to effectively constrain the process of show-through removal. Moreover, we constructed two datasets consisting of different show-through mixing ratios and conducted extensive experiments to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method achieves competitive performance compared to state-of-the-art methods and can effectively perform show-through removal without the need for paired datasets. Specifically, the proposed method achieves an average PSNR of 33.85 dB on our datasets, outperforming comparable methods by a margin of 0.89 dB.
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