Detect Larger at Once: Large-Area Remote-Sensing Image Arbitrary-Oriented Ship Detection

Ship detection needs to identify ship locations from remote sensing scenes. Due to different imaging payloads, various appearances of ships, and complicated background interference from the bird's eye view, it is difficult to setup a unified paradigm for achieving multisource ship detection. To address this challenge, in this article, leveraging the large language models powerful generalization ability, a unified visual-language model called Popeye is proposed for multisource ship detection from RS imagery. Specifically, to bridge the interpretation gap across the multisource images for ship detection, a novel unified labeling paradigm is designed to integrate different visual modalities and the various ship detection ways, i.e., horizontal bounding box and oriented bounding box. Subsequently, the hybrid experts encoder is designed to refine multiscale visual features, thereby enhancing visual perception. Then, a visual-language alignment method is developed for Popeye to enhance interactive comprehension ability between visual and language content. Furthermore, an instruction adaption mechanism is proposed for transferring the pretrained visual-language knowledge from the nature scene into the RS domain for multisource ship detection. In addition, the segment anything model is also seamlessly integrated into the proposed Popeye to achieve pixel-level ship segmentation without additional training costs. Finally, extensive experiments are conducted on the newly constructed ship instruction dataset named MMShip, and the results indicate that the proposed Popeye outperforms current specialist, open-vocabulary, and other visual-language models in zero-shot multisource various ship detection tasks.

Airport runways, as the core part of airports, belong to vital national infrastructure, and the target detection and segmentation of airport runways in remote sensing images using deep learning methods have significant research value. Most of the existing airport target detection methods based on deep learning rely on horizontal bounding boxes for localization, which often contain irrelevant background information. Moreover, when detecting multiple intersecting airport runways in a single remote sensing image, issues such as false positives and false negatives are apt to occur. To address these challenges, this study proposes an end-to-end remote sensing image airport runway detection and segmentation method based on an improved Mask RCNN (CR-Mask RCNN). The proposed method uses a rotated region generation network instead of a non-rotated region generation network, allowing it to generate rotated bounding boxes that fit the shape of the airport runway more closely, thus avoiding the interference of a large amount of invalid background information brought about by horizontal bounding boxes. Furthermore, the method incorporates an attention mechanism into the backbone feature extraction network to allocate attention to different airport runway feature map scales, which enhances the extraction of local feature information, captures detailed information more effectively, and reduces issues of false positives and false negatives when detecting airport runway targets. The results indicate that, when comparing horizontal bounding boxes with rotated bounding boxes for detecting and segmenting airport runways, the latter are more precise for complex backgrounds. Furthermore, incorporating an attention mechanism enhances the accuracy of airport runway recognition, making it highly effective and practical.

In recent years, data-driven deep learning has yielded fruitful results in synthetic aperture radar (SAR) ship detection; weakly supervised learning methods based on horizontal bounding boxes (HBBs) train oriented bounding box (OBB) detectors using HBB labels, effectively addressing scarce OBB annotation data and advancing SAR ship OBB detection. However, current methods for oriented SAR ship detection still suffer from issues such as insufficient quantity and quality of pseudo-labels, low inference efficiency, large model parameters, and limited global information capture, making it difficult to balance detection performance and efficiency. To tackle these, we propose the weakly supervised oriented SAR ship detection algorithm based on optimized pseudo-label generation and guidance. The method introduces pseudo-labels into a single-stage detector via a two-stage training process: the first stage coarsely learns target angles and scales using horizontal bounding box weak supervision and angle self-supervision, while the second stage refines angle and scale learning guided by pseudo-labels, improving performance and reducing missed detections. To generate high-quality pseudo-labels in large quantities, we propose three optimization strategies: Adaptive Kernel Growth Pseudo-Label Generation Strategy (AKG-PLGS), Pseudo-Label Selection Strategy based on PCA angle estimation and horizontal bounding box constraints (PCA-HBB-PLSS), and Long-Edge Scanning Refinement Strategy (LES-RS). Additionally, we designed a backbone and neck network incorporating window attention and adaptive feature fusion, effectively enhancing global information capture and multiscale feature integration while reducing model parameters. Experiments on SSDD and HRSID show that our algorithm achieves an mAP50 of 85.389% and 82.508%, respectively, with significantly reduced model parameters and computational consumption.

Lightweight ship detection is an important application of synthetic aperture radar (SAR). The prevailing trend in recent research involves employing a detection framework based on convolutional neural networks (CNNs) and horizontal bounding boxes (HBBs). However, CNNs with local receptive fields fall short in acquiring adequate contextual information and exhibit sensitivity to noise. Moreover, HBBs introduce significant interference from both the background and adjacent ships. To overcome these limitations, this paper proposes a lightweight transformer-based method for detecting arbitrarily oriented ships in SAR images, called LD-Det, which excels at promptly and accurately identifying rotating ship targets. First, light pyramid vision transformer (LightPVT) is introduced as a lightweight backbone network. Built upon PVT v2-B0-Li, it effectively captures the long-range dependencies of ships in SAR images. Subsequently, multi-scale deformable feature pyramid network (MDFPN) is constructed as a neck network, utilizing the multi-scale deformable convolution (MDC) module to adjust receptive field regions and extract ship features from SAR images more effectively. Lastly, shared deformable head (SDHead) is proposed as a head network, enhancing ship feature extraction with the combination of deformable convolution operations and a shared parameter structure design. Experimental evaluations on two publicly available datasets validate the efficacy of the proposed method. Notably, the proposed method achieves state-of-the-art detection performance when compared with other lightweight methods in detecting rotated targets.

Deep learning have been playing an increasing important role in ship detection in synthetic aperture radar (SAR) image because of its high accuracy, fast speed and no need of manual design features. It is difficult to set appropriate anchors when using anchor-based method because of multi-resolution SAR images and multi-scale ships, thus resulting in degradation of detection performance in operational applications. Recently, a lot of anchor-free architecture have been put forward, among which most of them apply horizontal bounding boxes (HBBs) to encode ship location, leading to inaccurate location description and decreased detection performance especially for arbitrary-oriented ships in complex scenes. Therefore, an anchor-free detector for arbitrary-oriented ship detection in SAR images, called R-FCOS is proposed in this paper. Specifically, based on the FCOS baseline method, ship aspect angle and rotate regression branch are introduced to regress oriented bounding boxes (OBBs) for arbitrary-oriented ships detection. Besides, to alleviate the effects of feature misalignment problem, ratio-ness branch is proposed to predict the aspect ratio of ship, which is used to filter and refine the OBBs in the post-processing step to improve detection accuracy. Finally, we carry out experiments on the SSDD+ data set to verify the effectiveness of our method. It turns out that our method obtains mAP 90.65% under an IoU threshold of 0.5.

Withcontinuous advancements in remote sensing technology, object detection in remote sensing images has emerged as a critical research direction in maritime surveillance, port management, and national defense. Among these applications, ship detection is a key task. Due to the fact that ships in images typically exhibit arbitrary rotations, multi-scale distributions, and complex backgrounds, conventional detection methods based on horizontal or rotated bounding boxes often fail to adequately capture the fine-grained information of the targets, thereby compromising detection accuracy. This paper proposes the Dense Representative Points-Guided Rotated-Ship Detection (DenseRRSD) method. The proposed approach represents ship objects using dense representative points (RepPoints) to effectively capture local semantic information, thereby avoiding the background noise issues associated with traditional rectangular bounding box representations. To further enhance detection accuracy, an edge region sampling strategy is devised to uniformly sample RepPoints from critical ship parts, and a Weighted Residual Feature Pyramid Network (WRFPN) is introduced to efficiently fuse the multi-scale features through residual connections and learnable weights. In addition, a Weighted Chamfer Loss (WCL) combined with a staged localization loss strategy is employed to progressively refine localization from coarse to fine stages. Experimental results on both the HRSC2016 dataset and the newly constructed DOTA-SHIP dataset demonstrate that DenseRRSD achieves state-of-the-art detection accuracy, with mean Average Precision (mAP) scores of 91.2% and 83.2%, respectively, significantly outperforming existing methods. These results verify the effectiveness and robustness of the proposed approach in rotated-ship detection under diverse conditions.

An operator feels a burden when he/she controls a rescue robot remotely because he/she has to keep watching camera images to find the target object. We think that this burden can be reduced by the combination of Head Mounted Display (HMD) and object recognition by deep learning. In the first half part of this study, we examine the effect that how presentation method by You Only Look Once (YOLO), a deep learning algorithm, and its recognition results to an operator wearing HMD. In the experiment, three methods of presentation were set: no display of object recognition, display only one object recognition result, and display 80 kinds of object recognition results. Under each presentation method, we measured the time it took for the operator to operate the robot and complete the given task. Additionally, we ask a questionnaire for each experiment. The results of the questionnaire showed that the method to present only one object recognition result was useful. In the second half part of this study, we develop a system to present 3D images with YOLO added, to further ease the burden of object search. Furthermore, we numerically prove that this system represents depth. In the experiment, two methods of displaying were set up: 2D images with Bounding Box (BB) by YOLO and 3D images with BB by YOLO. For each method of presentation, the operator operated the robot and recorded the number of objects found within a time limit. Additionally, we asked a questionnaire at the end of the search in each condition and at the end of all the experiments. The results of the questionnaire suggested points that need to be improved. Furthermore, we consider the flicker of the image found in the experiment.

Object detection has a great significance to remote sensing image recognition. Conventional object detection methods by using horizontal bounding box have shown good performance in general images. However, because of looking down perspective of remote sensing images, rotation bounding box is more suitable and precise for object detection. Although rotation bounding box has been researched by some works for remote sensing images, most of them are still two stages. In this paper, by improvement of YOLO architecture, we propose an one-stage object detection method to predict rotation bounding box for remote sensing images. Firstly, in the data preprocessing stage, an image cropping operation is utilized instead of zooming operation to generate suitable input data for network. Meanwhile, in order to avoid an object from being divided, an overlapping band is set during cropping. Secondly, a novel rotation bounding box representation method is introduced, and a corresponding loss function is designed in training process. Experiments on the Dota dataset demonstrate that our method outperforms state-of-the-art rotation objects detection methods, in terms of mAP, our method achieves 73.50%mAP, which is higher than methods based on rotation bounding box by 2.0%.

It is still challenging to effectively detect ship objects in optical remote-sensing images with complex backgrounds. Many current CNN-based one-stage and two-stage detection methods usually first predefine a series of anchors with various scales, aspect ratios and angles, and then the detection results can be outputted by performing once or twice classification and bounding box regression for predefined anchors. However, most of the defined anchors have relatively low accuracy, and are useless for the following classification and regression. In addition, the preset anchors are not robust to produce good performance for other different detection datasets. To avoid the above problems, in this paper we design a paired semantic segmentation network to generate more accurate rotated anchors with smaller numbers. Specifically, the paired segmentation network predicts four parts (i.e., top-left, bottom-right, top-right, and bottom-left parts) of ships. By combining paired top-left and bottom-right parts (or top-right and bottom-left parts), we can take the minimum bounding box of these two parts as the rotated anchor. This way can be more robust to different ship datasets, and the generated anchors are more accurate and have fewer numbers. Furthermore, to effectively use fine-scale detail information and coarse-scale semantic information, we use the magnified convolutional features to classify and regress the generated rotated anchors. Meanwhile, the horizontal minimum bounding box of the rotated anchor is also used to combine more context information. We compare the proposed algorithm with state-of-the-art object-detection methods for natural images and ship-detection methods, and demonstrate the superiority of our method.

The You Only Look Once (YOLO) network is considered highly suitable for real-time object detection tasks due to its characteristics, such as high speed, single-shot detection, global context awareness, scalability, and adaptability to real-world conditions. This work introduces a comprehensive analysis of various YOLO models for detecting cracks in concrete structures, aiming to assist in the selection of an optimal model for future detection and segmentation tasks. The YOLO models are initially trained on a dataset containing both images with and without cracks, producing a generalized model capable of extracting abstract features beneficial for crack detection. Subsequently, transfer learning is employed using a dataset that reflects real-world conditions, such as occlusions, varying crack sizes, and rotations, to further refine the model. Crack detection in concrete remains challenging due to the wide variation in crack sizes, aspect ratios, and complex backgrounds. To achieve optimal performance, we test different versions of YOLO, a state-of-the-art single-shot detector, and aim to balance inference speed and mean average precision (mAP). Our results indicate that YOLOv10 demonstrates superior performance, achieving a mean average precision (mAP) of 74.52% with an inference time of 19.5 milliseconds per image, making it the most effective among the models tested.

In recent years, in the field of remote sensing image target detection, oriented target detection has received extensive attention. Traditional detectors use horizontal bounding box (HBB) to locate targets, which may cause inaccurate detection when detecting targets with arbitrary orientation angles, dense distribution and large aspect ratios. The Oriented Bounding Box (OBB) adds different rotation angles to the horizontal bounding box, which can better deal with the above problems. However, with the introduction of oriented bounding boxes in the detector, new problems have emerged, such as the increase in the number of anchor points with different scales, angles, and aspect ratios, and the imbalance of IoU matching of positive and negative anchor boxes, which leads to classification confidence and Inconsistency between positioning accuracy. In order to overcome these shortcomings while using the oriented bounding box, this paper proposes a novel oriented target detector and redesigns a matching strategy for evaluating the position of the anchor frame. By selecting a higher quality anchor frame to achieve more accurate target detection, thereby reducing the instability of the angle in the matching process. Experiments conducted on public remote sensing data sets such as HRSC2016 and UCAS-AOD show that the proposed method achieves advanced detection accuracy.

Monitoring the spatial distribution of tailing ponds is of great significance for environmental governance in mining areas. Remote sensing technology is an effective tool to quickly obtain the location information of tailing ponds in large areas. However, most current studies about tailing pond detection focus on using images from single sensor and are with relatively low detection accuracy in complex backgrounds. In this paper, we present a framework to annotate the location of Tailing Ponds (TPs) from multi-source and high-resolution remote sensing images. In the proposed framework, an improved You Only Look Once (YOLO) deep learning based object detection model is employed, which embeds a high-density feature aggregation module, a low-parameter feature aggregation module and a refined loss function. The introductions of two new modules can make the network better capture global information about the object and accelerate the detection speed, while the introduction of a refined loss function can make the target box regression process more robust and avoiding divergence during training. To train reliable and robust YOLO based TP detection model, sufficient training samples in different backgrounds were collected, and some different sample augmentation methods were also employed. Experimental results validate that, the proposed YOLO based TP annotation method can accurately identify the targets with high annotation accuracies in complex backgrounds.

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.

The goal is to recognize different objects by applying the YOLO (You Only Look Once) technique. This technique has a few benefits in contrast to any other techniques for object detection and tracking. In some codes as Fast Convolutional Neural Network (FCNN) and Convolutional Neural Network (CNN) the code will not focus at the picture entirely but for the case of YOLO, the code focuses the entire image by concluding the detection boxes utilizing a convolutional neural framework and the probability of classes for the bounding boxes and finds the image immediately in contrast to some different codes. The dropout layer is also programed at the end to avoid over fitting issues. It is seen that using dropout the results have improved much.

Ship detection in synthetic aperture radar (SAR) images is crucial for maritime security and operations. However, existing methods face significant challenges when dealing with complex backgrounds and small ships. Ship targets are often misclassified as background clutter, and small ships are particularly susceptible to noise interference. To address these challenges, this paper proposes FFC-YOLO, an enhanced ship detection network based on YOLOv8. FFC-YOLO incorporates several novel modules to improve detection accuracy. Firstly, the Feature Attention Aggregation Module (FAAM) employs multi-scale fusion and Context Anchor Attention (CAA) to enhance detection in complex backgrounds by extracting features from different scales and integrating contextual information. Secondly, the Feature Augmentation Unit (FAU) enhances small ship detection accuracy by employing an improved deformable convolution (A-Defconv) for shape adaptation and an adaptive weighted fusion mechanism to refine feature extraction. Additionally, the Cross-Scale Dynamic Fusion Module (CSDF) dynamically integrates features from different scales, addressing information conflicts that arise when handling multi-scale ship targets, further enhancing detection accuracy. Finally, a novel bounding box loss function, CNW-loss, optimizes the overall network performance. Evaluated on the SSDD and HRSID datasets, FFC-YOLO achieves a significant performance advantage over existing methods, with mAP0.5 scores of 98.5% and 89.7%, respectively. The proposed FFC-YOLO network effectively enhances ship detection accuracy in SAR images, making it highly applicable to various maritime safety and operations, and providing valuable insights for a broader range of measurement applications.