Real-Time Video Processing for Ship Detection Using Transfer Learning

Due to the sizes of synthetic aperture radar (SAR) images are very large, traditional deep learning methods usually require the dense sliding window pre-processing to obtain sub-images for ship detection. Generally, most sub-images contain no ship targets, and they may bring a large amount of redundant information that greatly affects the detection efficiency. To deal with the above problems, this paper proposes a fast ship detection method for SAR images in the inshore region, which mainly consists of two stages: scene classification and ship detection. The two stages employ two cascade deep convolutional networks, i.e., scene classification network (SCN) and single shot detector (SSD), respectively. The SCN can quickly eliminate the sub-images that may not contain ships, and then the remaining sub-images are input into the SSD to implement refined ship target detection. The experimental results based on the real dataset AIR-SARShip-1.0 show that the proposed method has higher efficiency than the original SSD method while maintaining relative high accuracy.

Ship detection and classification is critical for national maritime security and national defense. Although some SAR (Synthetic Aperture Radar) image-based ship detection approaches have been proposed and used, they are not able to satisfy the requirement of real-world applications as the number of SAR sensors is limited, the resolution is low, and the revisit cycle is long. As massive optical remote sensing images of high resolution are available, ship detection and classification on theses images is becoming a promising technique, and has attracted great attention on applications including maritime security and traffic control. Some digital image processing methods have been proposed to detect ships in optical remote sensing images, but most of them face difficulty in terms of accuracy, performance and complexity. Recently, an autoencoder-based deep neural network with extreme learning machine was proposed, but it cannot meet the requirement of real-world applications as it only works with simple and small-scaled data sets. Therefore, in this paper, we propose a novel ship detection and classification approach which utilizes deep convolutional neural network (CNN) as the ship classifier. The performance of our proposed ship detection and classification approach was evaluated on a set of images downloaded from Google Earth at the resolution 0.5m. 99% detection accuracy and 95% classification accuracy were achieved. In model training, 75× speedup is achieved on 1 Nvidia Titanx GPU.

With the rapid growth of global maritime traffic, effective ship detection from satellite and aerial images has become essential for applications in maritime security, disaster response, and environmental monitoring. Traditional ship detection methods struggle with the challenges posed by the vastness, complexity, and dynamic nature of oceanic environments. To address these challenges, we propose a highly efficient model that combines the power of CNN with the RPN for fast and accurate ship detection. This study introduces a novel approach for real-time ship object detection in RSI images using the Faster R-CNN. The proposed model is optimized for real-time processing, ensuring the rapid detection of ships even in large-scale satellite imagery. By utilizing a comprehensive dataset of high-resolution RSI images, the system is trained to recognize ships under varying conditions, including different angles, lighting, and sea states. The Faster R-CNN model demonstrates superior performance, offering both high detection accuracy and fast inference times, making it suitable for deployment in real-world maritime surveillance systems. The proposed approach outperformed more conventional R-CNN models in terms of accuracy (99.25 percent for ResNet-50) in experiments conducted on the ship object benchmark dataset.

In this paper, we present a novel method to detect the ship in harbor based on image segmentation for SAR imagery. According to the position of the ship in harbor, we divide them into the offshore ship and inshore ship, and different strategies are implemented for ship detection. First, we use the sea-land segmentation method to separate SAR image into land region and sea region, and then extract buffer region according to the coastline. Second, we employ the TS-CFAR detector which has a state-of-the-art performance in multiple-target situations to achieve offshore ship detection. Finally, for the inshore ship, we propose a region-based saliency detection to complete the ship detection. The region-based saliency detection method can tolerate a certain degree of speckle noise. Experimental results show that the proposed method is robust, efficient and can detect different kinds of ship in the harbor.

Algorithms based on deep learning are taking off as solutions to several problems in numerous fields. Numerous deep learning architectures like convolutional neural networks, recurrent neural networks, deep belief networks, artificial neural networks and deep neural networks have been used in fields such as audio recognition, machine vision, processing of natural language, recognition of speech, social network filtering, computer vision, translating languages and bioinformatics. One such application of deep learning architectures is object detection. Object detection in satellite and aerial imagery undertakes an important role in a long list of applications and has allured a lot of attention which resulted in exceptional progress in recent years. It has abundant uses in various fields such as surveillance, military, national security, urban planning, environmental applications, traffic control and forestry. Several issues like varying size, background and orientation of the target object make the automatic detection of target in satellite imagery a challenging issue. Recent research in this field has given better speed and accuracy to the detection of the target object. With the rise in the data being generated by the Earth Observation programs, it is becoming much more imperative to find better and efficient ways to exploit this data by improving upon the existing Deep Learning research. In this paper, we bring forth a wide-ranging analysis of the recent advancements in methods of object detection based on deep learning to recognize the objects in satellite imagery.KeywordsDeep learningObject detectionSatellite imageryRemote sensingYou Only Look Once (YOLO)You Only Look Twice (YOLT)Single Shot Detector (SSD)Convolutional Neural Networks (CNN)Faster Region-based Convolutional Neural Networks (Faster RCNN)Hybrid Deep Convolutional Neural Network (HDNN)Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN)

With the advancements in Space technology and the development of light-weight Synthetic Aperture Radar (SAR) satellites by commercial companies such as ICEYE, Capella Space and Umbra, SAR images have become available on a wide scale. Ship detection is a classic problem in the interpretation and analysis of satellite images and has its significance both in maritime as well as defense applications. In the case of SAR images, ship detection becomes even more challenging due to the presence of large-scale distortions as well as inter-class similarity signature problem. Moreover, the State-of-the-Art (SOTA) object detection models have weak generalization capability over SAR datasets. To overcome these challenges, we propose a You Only Look Once (YOLO) based, optimized ship detection model called YOLO-OSD. Our optimized ship detector is based on a hybrid data-model centric approach which utilizes the statistical characteristics of the datasets under observation and has an efficient model architecture. We also carry out a detailed comparative analysis of our proposed model with other SOTA deep learning models on three well-known publicly available datasets. Our results show that the proposed YOLO-OSD outperforms YOLO5, YOLO7 and RetinaNet on all datasets under observation in terms of F1 score and mean Average Precision (mAP). YOLO-OSD also has approximately 16% fewer network parameters as compared to the original YOLO5. Moreover, our proposed model is at least 37.7% faster than YOLO7 and 41.02% faster than the YOLO8 model in terms of training time and thus suitable for real-time satellite based SAR ship detection.

Ship detection in optical remote sensing images has potential applications in national maritime security, fishing, and defense. Many detectors, including computer vision and geoscience-based methods, have been proposed in the past decade. Recently, deep-learning-based algorithms have also achieved great success in the field of ship detection. However, most of the existing detectors face difficulties in complex environments, small ship detection, and fine-grained ship classification. One reason is that existing datasets have shortcomings in terms of the inadequate number of images, few ship categories, image diversity, and insufficient variations. This article publishes a public ship detection dataset, namely ShipRSImageNet, which contributes an accurately labeled dataset in different scenes with variant categories and image sources. The proposed ShipRSImageNet contains over 3435 images with 17 573 ship instances in 50 categories, elaborately annotated with both horizontal and orientated bounding boxes by experts. From our knowledge, up to now, the proposed ShipRSImageNet is the largest remote sensing dataset for ship detection. Moreover, several state-of-the-art detection algorithms are evaluated on our proposed ShipRSImageNet dataset to give a benchmark for deep-learning-based ship detection methods, which is valuable for assessing algorithm improvement. 1

Remote sensing data represent one of the most important sources for automized yield prediction. High temporal and spatial resolution, historical record availability, reliability, and low cost are key factors in predicting yields around the world. Yield prediction as a machine learning task is challenging, as reliable ground truth data are difficult to obtain, especially since new data points can only be acquired once a year during harvest. Factors that influence annual yields are plentiful, and data acquisition can be expensive, as crop-related data often need to be captured by experts or specialized sensors. A solution to both problems can be provided by deep transfer learning based on remote sensing data. Satellite images are free of charge, and transfer learning allows recognition of yield-related patterns within countries where data are plentiful and transfers the knowledge to other domains, thus limiting the number of ground truth observations needed. Within this study, we examine the use of transfer learning for yield prediction, where the data preprocessing towards histograms is unique. We present a deep transfer learning framework for yield prediction and demonstrate its successful application to transfer knowledge gained from US soybean yield prediction to soybean yield prediction within Argentina. We perform a temporal alignment of the two domains and improve transfer learning by applying several transfer learning techniques, such as -SP, BSS, and layer freezing, to overcome catastrophic forgetting and negative transfer problems. Lastly, we exploit spatio-temporal patterns within the data by applying a Gaussian process. We are able to improve the performance of soybean yield prediction in Argentina by a total of 19% in terms of RMSE and 39% in terms of compared to predictions without transfer learning and Gaussian processes. This proof of concept for advanced transfer learning techniques for yield prediction and remote sensing data in the form of histograms can enable successful yield prediction, especially in emerging and developing countries, where reliable data are usually limited.

Object Detection Technology has been a subject to much research and development due to increasing use of images and videos as data sources and their huge number of applications. Traditional models for object detection had limitations in training and did not use transfer learning for their benefit. With the evolution of deep learning and Neural networks, newer and powerful tools have made way to achieve Object Detection in real-time, with the added advantage of transfer learning and detection of multiple instances of different classes of interest in the given image context. The proposed system is an Object Detection model based on the Single Shot Detector (SSD) algorithm trained with MobileNetV2 feature extraction that can be utilized and integrated in e-commerce, hospitality industry, security and surveillance, real estate, self-driving cars and floor inventory management.

Images created using the Super Resolution method can generate more information compared to their low resolution counterparts. A super-resolved image, which is created using an original image captured by an imaging source is not only more meaningful to human perception but also has advantages on downstream tasks such as object detection and pattern recognition. In this work, we aim to apply the Super Resolution method to the Aerial Images captured for surveillance to enable more information about the original scenes. To achieve this Super Resolution Generative Adversarial Network (SRGAN), which is based on the Generative Adversarial Networks architecture is used. We also applied transfer learning methodology to achieve better image quality. Public xView and DOTA datasets which contain images mostly captured by satellites around the world are used to train a generative model via SRGAN architecture. Furthermore, DIV2K dataset is used to pre-train a generative model, and then the transfer learning technique is used to train separate models on xView and DOTA validation datasets. Perceptual Index (PI) and Root Mean Squared Error (RMSE) which are used on European Conference on Computer Vision -Perceptual Image Restoration and Manipulation Workshop 2018 are computed as the performance metrics. We have seen that the model which gives the best PI results, i.e. better perceptual quality, on xView and DOTA validation datasets is the one trained using the DIV2K dataset and the model which gives the best RMSE results, i.e. better reconstruction quality, is the one trained using the transfer learning technique.

Automatic ship detection from optical satellite images remains a tough task. In this paper, a novel method of ship detection from optical satellites is proposed by analyzing the ship distribution probability. First, an anomaly detection model is constructed by the sea cluster histogram model; then, the ship distribution based on the ship safety navigational criterion is analyzed to obtain the ship candidates, and obvious non-ship objects are removed by the area properties from ship candidates; finally, a structural continuity descriptor is designed to remove false alarms from the ship candidates. Experiments on numerous satellite images from panchromatic and one band within multispectral sensors are conducted. The results verified that the proposed method outperforms existing methods in both effectiveness and efficiency.

This paper proposes an improved method for autonomous landing scene detection for drones. The study addresses challenges that occur when similar environments appear different at various altitudes. Using deep learning methods and a hybrid ensemble technique, the proposed system improves the accuracy and reliability of landing scene recognition. The proposed system achieved approximately 97.65% accuracy using transfer learning models such as ResNet50 and ResNext50 combined with a hybrid Random Forest classifier. Transfer learning techniques using ResNet50 and ResNeXt50 models are applied to the LandingScenes-7 dataset to identify safe landing locations in real time. The thresholding techniques and novelty detection module enable the system to handle unpredictable environmental conditions and provide confidence-based classification decisions. This research has significant applications in drone technology, particularly in logistics, emergency response, and surveillance. The proposed system enhances drone intelligence and improves operational safety in dynamic environments by enabling reliable autonomous landing decisions.

<p class="VARAbstract">The growing availability of three-dimensional (3D) data, such as point clouds, coming from Light Detection and Ranging (LiDAR), Mobile Mapping Systems (MMSs) or Unmanned Aerial Vehicles (UAVs), provides the opportunity to rapidly generate 3D models to support the restoration, conservation, and safeguarding activities of cultural heritage (CH). The so-called scan-to-BIM process can, in fact, benefit from such data, and they can themselves be a source for further analyses or activities on the archaeological and built heritage. There are several ways to exploit this type of data, such as Historic Building Information Modelling (HBIM), mesh creation, rasterisation, classification, and semantic segmentation. The latter, referring to point clouds, is a trending topic not only in the CH domain but also in other fields like autonomous navigation, medicine or retail. Precisely in these sectors, the task of semantic segmentation has been mainly exploited and developed with artificial intelligence techniques. In particular, machine learning (ML) algorithms, and their deep learning (DL) subset, are increasingly applied and have established a solid state-of-the-art in the last half-decade. However, applications of DL techniques on heritage point clouds are still scarce; therefore, we propose to tackle this framework within the built heritage field. Starting from some previous tests with the Dynamic Graph Convolutional Neural Network (DGCNN), in this contribution close attention is paid to: i) the investigation of fine-tuned models, used as a transfer learning technique, ii) the combination of external classifiers, such as Random Forest (RF), with the artificial neural network, and iii) the evaluation of the data augmentation results for the domain-specific ArCH dataset. Finally, after taking into account the main advantages and criticalities, considerations are made on the possibility to profit by this methodology also for non-programming or domain experts.</p><p>Highlights:</p><ul><li><p>Semantic segmentation of built heritage point clouds through deep neural networks can provide performances comparable to those of more consolidated state-of-the-art ML classifiers.</p></li><li><p>Transfer learning approaches, as fine-tuning, can considerably reduce computational time also for CH domain-specific datasets, as well as improve metrics for some challenging categories (i.e. windows or mouldings).</p></li><li><p>Data augmentation techniques do not significantly improve overall performances.</p></li></ul>

Nowadays, ship detection in sea-sky background is not only useful in maritime visual surveillance, but also helpful in maritime search and rescue. Since ships are salient objects in infrared images with sea-sky background, we present a novel and effective algorithm based on saliency for ship detection in this situation. Our algorithm adopts global saliency, local saliency and background prior to generate saliency maps. Ships are finally segmented in saliency maps. Our algorithm is compared with four classic salient object detection algorithms. And experimental results show our algorithm outperforms the other four algorithms in qualitative and quantitative terms. Keyword list: ship detection ; salient object detection ; infrared image ; sea - sky background . 1. INTRODUCTION Since ships are important vehicles for sea transportation, ship detection in sea-sky background is extensively used in maritime visual surveillance and reconnaissance, avoidance of ship collision accidents, maritime search and rescue, etc. Infrared imaging systems can almost work in any environment and all-weather conditions with its visual capacity in darkness and frog, which makes it a very suitable tool in this application. In recent years, ship detection in infrared images with sea-sky background attracts increasing attention. Many methods are proposed in this field, including image segmentation methods [1-2] and morphological methods [3]. However, due to complex sea-sky environment and large variation of ship sizes, these methods cannot solve the issue completely. And since ships are often salient objects in infrared images with sea-sky background, some researchers have recently tried salient object detection ideas used in color images [4-6]. And the paper is focused on using salient object detection methods to solve the issue. Salient objects are objects which stand out in the image and attract most people ¶VLPPHGLDWHDWWHQWLRQ7KHVWDQGDUGway to detect salient ob MHFWVLVILUVWO\XVLQJVDOLHQF\GHWHFWLRQPHWKRGVWRJHQHUDWHVDOLHQF\PDSVLQZKLFKDSL[HO¶VJUH\value is proportional to its degree of saliency and then segmenting salient objects in saliency maps. Many salient detection methods can be divided into three categories: local contrast methods, global rarity methods and other methods. In local contrast methods, regions which have high contrast with local neighbouring regions are regarded as salient regions. Itti et al. [7] decompose an image into several feature maps . The saliency in each feature map is measured by local center-surround difference in several scales and saliency of different feature maps are integrated to a final saliency

In India a large part of goods transportation is carried out by sea, leading to an emerging requirement for remote maritime patrolling system, which also serves as an asset during wartime and peacetime for defence. In this research paper, we propose an automated maritime patrolling solution by making a Deep Learning Model Pipeline for Ship Detection from satellite images using existing State of the art Object Detection Algorithms like Faster-RCNN, SSD, YOLOv3, and YOLOv4. We compare results based on various Evaluation Metrics. Further we also release our own dataset which consists of around 300 satellite images of the top 13 busiest Sea-ports of India. After performing the validations, we found that the YOLO v4 displayed the best re-sults with a balanced mAP and FPS score to detect the ships in the satellite images.