MODAN: Multifocal Object Detection Associative Network for Maritime Horizon Surveillance

Microvascular invasion (MVI) is an important predictor of postoperative recurrence or poor outcomes of hepatocellular carcinoma (HCC). Radiomics is able to predict MVI in HCC preoperatively. This study aims to investigate the influence of different region of interest (ROI) sizes on CT-based radiomics model for MVI prediction in HCC. Patients with HCC with or without MVI confirmed by pathology and those who underwent preoperative plain or enhanced abdominal CT scans in the Third Xiangya Hospital of Central South University from January 2010 to December 2020 were retrospectively and consecutively included. According to the ratio of 7 to 3, the patients were randomly assigned into a training set and a validation set. Clinical data were collected from medical records, and radiomics features were extracted from the arterial phase (AP) and portal venous phase (PVP) of preoperatively acquired CT in all patients. Six different ROI sizes were employed. The original ROI (OROI) was manually delineated along the visible borders of the tumor layer-by-layer. The OROI was expanded out by 1-5 mm. The OROI was combined with 5 different peritumoral regions to generate the other 5 ROIs, named Plus1-Plus5. Feature extraction, dimension reduction, and model development were conducted in 6 different ROIs separately. Supporter vector machine (SVM) was used for model construction. Model performance was assessed via receiver operating characteristic (ROC) curve. A total of 172 HCC patients were included, in which 83 (48.3%) were MVI positive, and 89 (51.7%) were MVI negative. Three hundred and ninety-six features based on AP or PVP images were extracted from each ROI. After feature selection and dimension reduction, 4, 5, 15, 11, 6, and 3 features of OROI, Plus1, Plus2, Plus 3, Plus4, and Plus5 were selected for model construction, respectively. In the training set, the sensitivity, specificity, and area under the curve (AUC) of OROI were 0.759, 0.806, and 0.855, respectively. The AUC values of Plus2 (0.979) and Plus3 (0.954) were higher than that of OROI. The AUC values of Plus1 (0.802), Plus4 (0.792), and Plus5 (0.774) were not significantly different from those of OROI. In the validation set, the sensitivity, specificity, and AUC value of OROI were 0.640, 0.630, and 0.664, respectively. The AUC value of Plus3 was 0.903, which was higher than that of OROI. The AUC values of Plus1 (0.679), Plus2 (0.536), Plus4 (0.708), and Plus5 (0.757) were not significantly different from that of OROI (P>0.05). The size of ROI significantly inflluences on the performance of CT-based radiomics model for MVI prediction in HCC. Including appropriate area around the tumor into ROI could improve the predictive performance of the model, and 3 mm might be appropriate distance.

Sandstorm weather significantly affects the quality of remote sensing images captured by drones, and collecting sandstorm datasets presents various challenges. This paper introduces a method for synthesizing a mild sandstorm dataset using Cycle-Consistent Adversarial Networks (CycleGAN), termed Sand-CycleGAN, which aimed to create a new sandstorm dataset, Sand-VisDrone, for the drone dataset VisDrone. This approach enhances the performance of remote sensing images in sandstorm conditions. To ensure that the synthesized dataset accurately reflects these conditions, we implement a Scatter Loss based on an atmospheric scatter model. This loss function improves the details and textures of the generated images, thereby increasing their realism. Additionally, we use a Super-Resolution Convolutional Neural Network (SRCNN) to enhance image resolution and restore detailed features that may be lost during the synthesis process. To validate the effectiveness of the Sand-VisDrone dataset, we conducted experiments with various dust removal networks and evaluated their performance using both reference-based and no-reference image quality metrics. The study also examines the influence of sandstorm image augmentation on the performance of object detection algorithms. Training results show that the mean Average Precision at IoU=0.5 (mAP@0.5) improved by 1.3% and 6.8% when using You Only Look Once version 10 narrow (YOLOv10n) and Real-Time Detection with Transformer for Object Tracking and Detection (RT-DETR) object detection algorithms, respectively.

Targeted-ROI imaging in electron paramagnetic resonance imaging

In this paper, a flexible region of interest (ROI) image coding method so-called partial block bitplane shift (PBBShift) is presented for JPEG2000. The new method offers two strategies - bitplane block scaling (BBS) and partial bitplane shift (PBS), which support arbitrarily shaped single or multiple ROI. In BBS, all bitplanes of ROI and background (BG) are divide into four parts - most significant ROI (MSR), general significant ROI (GSR), general significant BG (GSB), and least significant BG (LSB). In PBS, all bitplanes of MSR are shifted up over all residual bitplanes. The bitplanes in LSB are shifted down below all bitplanes. The bitplanes of GSR and GSB are not shifted. Experimental result shows that the presented method can flexibly adjust the compression quality between ROI and background by selecting an arbitrary scaling value without any ROI shape information. Additionally, PBBShift can efficiently code multiple ROIs with different degrees of interest in an image.

We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.

Offshore and inland river ship detection has been studied on both synthetic aperture radar (SAR) and optical remote sensing imagery. However, the classic ship detection methods based on SAR images can cause a high false alarm ratio and be influenced by the sea surface model, especially on inland rivers and in offshore areas. The classic detection methods based on optical images do not perform well on small and gathering ships. This paper adopts the idea of deep networks and presents a fast regional-based convolutional neural network (R-CNN) method to detect ships from high-resolution remote sensing imagery. First, we choose GaoFen-2 optical remote sensing images with a resolution of 1 m and preprocess the images with a support vector machine (SVM) to divide the large detection area into small regions of interest (ROI) that may contain ships. Then, we apply ship detection algorithms based on a region-based convolutional neural network (R-CNN) on ROI images. To improve the detection result of small and gathering ships, we adopt an effective target detection framework, Faster-RCNN, and improve the structure of its original convolutional neural network (CNN), VGG16, by using multiresolution convolutional features and performing ROI pooling on a larger feature map in a region proposal network (RPN). Finally, we compare the most effective classic ship detection method, the deformable part model (DPM), another two widely used target detection frameworks, the single shot multibox detector (SSD) and YOLOv2, the original VGG16-based Faster-RCNN, and our improved Faster-RCNN. Experimental results show that our improved Faster-RCNN method achieves a higher recall and accuracy for small ships and gathering ships. Therefore, it provides a very effective method for offshore and inland river ship detection based on high-resolution remote sensing imagery.

So far, there exist many publicly available palmprint databases. However, not all of them have provided the corresponding region of interest (ROI) images. If everyone uses their own extracted ROI images for performance testing, the final accuracy is not strictly comparable. Since ROI localization is the critical stage of palmprint recognition. The location precision has a significant impact on the final recognition accuracy, especially in unconstrained scenarios. This problem has limited the applications of palmprint recognition. However, many currently published surveys only focus on feature extraction and classification methods. Throughout these years, many new ROI localization methods have been proposed. In this chapter, we will group the existing ROI localization methods into different categories, analyze their basic ideas, reproduce some of the codes, make comparisons of their performances, and provide further directions. We hope this could be a useful reference for further research.

Insulators are widely used in distribution network transmission lines and serve as critical components of the distribution network. The detection of insulator faults is essential to ensure the safe and stable operation of the distribution network. Traditional insulator detection methods often rely on manual identification, which is time-consuming, labor-intensive, and inaccurate. The use of vision sensors for object detection is an efficient and accurate detection method that requires minimal human intervention. Currently, there is a considerable amount of research on the application of vision sensors for insulator fault recognition in object detection. However, centralized object detection requires uploading data collected from various substations through vision sensors to a computing center, which may raise data privacy concerns and increase uncertainty and operational risks in the distribution network. Therefore, this paper proposes a privacy-preserving insulator detection method based on federated learning. An insulator fault detection dataset is constructed, and Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models are trained within the federated learning framework for insulator fault detection. Most of the existing insulator anomaly detection methods use a centralized model training method, which has the advantage of achieving a target detection accuracy of over 90%, but the disadvantage is that the training process is prone to privacy leakage and lacks privacy protection capability. Compared with the existing insulator target detection methods, the proposed method can also achieve an insulator anomaly detection accuracy of more than 90% and provide effective privacy protection. Through experiments, we demonstrate the applicability of the federated learning framework for insulator fault detection and its ability to protect data privacy while ensuring test accuracy.

Limited by diffraction limit, low spatial resolution is one of the shortcomings of terahertz imaging. Low spatial resolution is also one of the reasons limiting the development of stress measurement using terahertz imaging. In this paper, the full-field stress measurement using Terahertz Time Domain Spectroscopy (THz-TDS) is combined with Super-Resolution Convolutional Neural Network (SRCNN) algorithm to obtain stress fields with high spatial resolution. A modulation model from a plane stress state to a THz-TDS signal is constructed. A large number of simulated sets are obtained to train the SRCNN model. By applying the trained SRCNN model to imaging the numerical and physical stress fields, the improved spatial resolution of stress field calculated from the captured THz-TDS signal is obtained.

In the domains that deal with a large scale image applications force the use of image compression in order to reduce the required storage. Due to the constrained bandwidth and storage capacity in the field of marine a new compression technique for the compression of the satellite images is proposed here. In our system, we are separating the region of Region of Interest (ROI) part from the original image. And this ROI images can be used for compression scheme by an algorithm known as the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The SPIHT algorithm is a part of the Lossless compression algorithm. The performance of our method can be done by calculating the PSNR values of the output images.

By changing the design paradigm for radiographic detectors to optimize imaging of a region of interest (ROI) for endovascular interventions, a new class of micro-angiographic detectors is proposed. Such ROI imagers optimized for high spatial resolution over a fraction of the conventional FOV accept compromises in x-ray absorption so that the desired high frequency DQE is achieved. A prototype demonstration system based upon a CsI(Tl) phosphor coupled by a fiber taper to a CCD is compared with an image-intensifier-based digital angiographic (DA) system for imaging typical neuro-vascular pathologies (stenoses, aneurysms, arterio-venous malformations (AVMs) and a variety of stents with wire diameter down to 50 micrometer. Although the zero frequency DQE of the DA unit exceeded that of the prototype with its thin phosphor layer, the prototype excelled in the desired frequency range of 3 - 10 lp/mm. Using an artery block phantom, the smallest 1 mm diameter stenoses and aneurysms were clearly visualized only with the prototype. For imaging stents, details of wires and struts were only visible with the prototype. ROI images of an AVM pig rete model showed more detailed angio-architecture compared to blurred-appearing DA images. It is expected that future such ROI cameras should allow improved clinical interventions.

In this paper, we design a cellular automata (CA)-based ROI (region of interest) image encryption system that can effectively reduce computational cost and maintain an appropriate level of security. The proposed image encryption system obtains a cryptographic image through three steps. First, a region of interest with high importance is extracted from the entire image using deep learning. We use the YOLO (You Only Look Once) algorithm to extract the ROI from a given original image. Next, the detected ROI is encrypted using the Chen system, a chaotic-based function with high security. Finally, the execution time is effectively reduced by encrypting the entire image using a hardware-friendly CA. The safety of the proposed encryption system is verified through various statistical experiment results and analyses.

Medical image is usually comprised of region of interest (ROI) and non region of interest (NROI). ROI is the region that contains the important information from diagnosis point of view so it must be stored without any distortion. We have proposed a digital watermarking technique which avoids the distortion of image in ROI by embedding the watermark information in NROI. The watermark is comprised of patient information, hospital logo and message authentication code, computed using hash function. Earlier BCH encryption of watermark is performed to ensure inaccessibility of embedded data to the adversaries.

We propose a fast, yet simple algorithm to find the region of interest (ROI) from a compressed MPEG video bitstream, with partial decoding. We have used the properties of the human visual system (HVS), applied in the DCT domain, to extract the ROIs. Though a lot of work has been done to obtain ROIs in images based on the HVS, all these systems work in the pixel domain. However, finding the ROIs in the compressed domain has wide applications such as emphasizing perceptually important regions while transrating, defining descriptors for encoded video (MPEG-7), changing image size to adapt to heterogeneous client displays, etc.

Deep Learning image processing methods are gradually gaining popularity in a number of areas including medical imaging. Classification, segmentation, and denoising of images are some of the most demanded tasks. In this study, we aim at enhancing optic nerve head images obtained by Optical Coherence Tomography (OCT). However, instead of directly applying noise reduction techniques, we use multiple state-of-the-art image Super-Resolution (SR) methods. In SR, the low-resolution (LR) image is upsampled to match the size of the high-resolution (HR) image. With respect to image enhancement, the upsampled LR image can be considered as low quality, noisy image, and the HR image would be the desired enhanced version of it. We experimented with several image SR architectures, such as super-resolution Convolutional Neural Network (SRCNN), very deep Convolutional Network (VDSR), deeply recursive Convolutional Network (DRCN), and enhanced super-resolution Generative Adversarial Network (ESRGAN). Quantitatively, in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), the SRCNN, VDSR, and DRCN significantly improved the test images. Although the ERSGAN showed the worst PSNR and SSIM, qualitatively, it was the best one.