A REAL-TIME SHIP OBJECT DETECTION IN REMOTE SENSING IMAGE USING FASTER REGION-CNN MODEL

Ship detection based on remote sensing image can provide effective assistance for maritime reconnaissance, port and fisheries management. This paper mainly presents an improved Faster R-CNN algorithm for ship detection with dilated convolution. Firstly, the image of remote sensing ship is preprocessed. This paper uses dark channel prior method to dehaze image and enhance dehazed image quality by grey world algorithm. Second, The Faster R-CNN is improved in ship detection. The dilated convolutional is introduced into Faster R-CNN to increases its feature extraction capability. Finally, ship detection software platform is built. The remote ship image preprocessing and ship detection network is integrated into an application software. The ship detection network can be trained end to end. Experimental results on the HRSC2016 datasets illustrated that the proposed presents a good performance for ship detection task.

The object detection approaches in conjunction with Fast/Faster R-CNN and YOLO have shown the benchmarking performance on several occasions. Inspired by the Refine Net, we propose a new model called Faster+ R-CNN based on Faster R-CNN, which is mainly based on iterative refinement on the proposed regions. The Faster+ R-CNN model can iteratively refine the region proposal based on previous output. We trained and tested our new model on PASCAL VOC 2007 dataset, and experiments showed that our method can iteratively improve the mean average precision (mAP) from 0.6702 to 0.6764 in object detecting task. We also demonstrate the facial detection results using the Faster+ R-CNN on the widely used Face Detection Dataset and Benchmark (FDDB) benchmark. By training the Faster+ R-CNN model on the large scale WIDER face dataset, we report the improved results on two widely used face detection benchmarks including FDDB.

At present, the ship detection of optical remote sensing images based on deep learning has made great progress. However, due to the different use scenarios and specific tasks, how to select an appropriate algorithm according to the characteristics of the target and the target priority, so that achieve the detection goal while consider the detection accuracy and speed, still requires relevant research. In this paper, ship detection methods for optical remote sensing images are studied based on deep learning. First, to meet the needs of ship detection research, according to the characteristics of target size and type, datasets of medium and large ships and small target ships are made, and model training and testing are conducted based on Faster R-CNN, YOLOv4, and SSD algorithms respectively. The actual detection performance of the three algorithms under different ship sizes is obtained. The results show that for medium and large targets, Faster R-CNN has the highest precision, the next is YOLOv4, and SSD is the lowest. The detection speed is that SSD is the fastest, the next is YOLOv4, Faster R-CNN is the slowest. For small target ship detection, YOLOv4 has the best detection accuracy and SSD has the fastest detection speed. Faster R-CNN is not as accurate and fast as the other two algorithms. In addition, for different type ships, the detection results of different algorithms also have some differences. In practical applications, different methods should be used to achieve detection by comprehensively considering such factors as target size, target priority, detection accuracy and speed requirements.

Automatic ship classification and detection is an interesting research field concerning maritime security. Automatic ship detection systems are important for maritime security and surveillance. These systems can be used to monitor marine traffic, illegal fishing and illegal activities which deal with the prospects of maritime security. This research has gained interest because many ships that are sailing on the ocean or sea do not install transponders which are used for tracking the ships. It will be a serious threat to the nation, mankind, and sea-life if we do not keep an eye on these kinds of ships. Therefore, in this we presented a novel Deep Learning method that will be used to detect ships by using satellite Images. This approach uses TensorFlow object detection API to detect objects in the images as we are concerned with object detection. The dataset used for this purpose is Maritime Satellite Imagery (MASATI)-v2 dataset which consists of various satellite images that are captured under different weather and dynamic conditions. As the real-time satellite monitoring is a kind of video thing, we proposed an approach to perform video processing to detect ships by using the model that is trained using MASATI-v2 dataset. For training the model we are using a Transfer Learning technique by utilizing (Single Shot Detector) SSD MobileNetV2. This algorithm is different from normal convolution neural networks, and it uses depth wise and pointwise separable filters to perform the task. KeywordsShip detectionTransfer learningObject detectionSingle shot detectorMobileNetV2

Maritime target detection is important part of intelligent ship’s perceptual system, Traditional method of extracting artificial features are inefficient and has poor generalization, this paper proposes deep learning to automatically acquire deep features of other targets, Faster R-CNN is adopted for target’s recognition and location; Resnet will replace VGG16 as the main framework of detection algorithm; What’s more, in order to improve the detection effect of the model in complex marine environment, it combines hard example mining. Then the model was trained and tested by self-made Pascal VOC2007 dataset. The experimental results show that the method can effectively identify the targets of different types of ships and has higher accuracy of detection.

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.

Abstract. The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model for building detection in single aerial image. As we know, the single aerial image is in vertical view and the buildings possess significant directional feature. However, in R-CNN model, direction of the building is ignored and the detection results are represented by horizontal rectangles. For this reason, the detection results with horizontal rectangle cannot describe the building precisely. To address this problem, in this paper, we proposed a novel model with a key feature related to orientation, namely, Oriented R-CNN (OR-CNN). Our contributions are mainly in the following two aspects: 1) Introducing a new oriented layer network for detecting the rotation angle of building on the basis of the successful VGG-net R-CNN model; 2) the oriented rectangle is proposed to leverage the powerful R-CNN for remote-sensing building detection. In experiments, we establish a complete and bran-new data set for training our oriented R-CNN model and comprehensively evaluate the proposed method on a publicly available building detection data set. We demonstrate State-of-the-art results compared with the previous baseline methods.

The development of a ship detection system using SAR pictures loaded with noise poses issues for pictures Intelligence (IMINT). The YOLOv8 model is utilized for ship identification. The preprocessing approaches entail employing a fusion of grayscale morphology techniques and image restoration using a harmonic mean filter and a bandpass. This technique is designed to assess the effect of noise reduction to enhance the accuracy of detecting objects in SAR images. The preprocessing technique is categorized into two methods: basic grayscale morphology (GM1-GM6) and a fusion of image restoration with grayscale morphology (GHB1-GHB6). The model's performance is assessed using mAP and IoU criteria. This research discovered that ship objects were not detected successfully in the presence of several types of noise. These failures were attributed to factors such as tiny ship size, low picture quality, and inadequate preprocessing techniques for noise handling. The findings indicate a substantial enhancement in ship detection, specifically in synthetic aperture radar (SAR) images affected by sidelobe noise. There were noticeable enhancements in the accuracy of images that underwent preprocessing using GHB5. GHB5 employs a combination of image restoration, closure, and erosion techniques.

Indian traffic sign detection and recognition using deep learning

High-resolution synthetic aperture radar (SAR) data have been widely used in marine environmental protection, marine environmental monitoring, and marine traffic management. Ship detection is one of the important parts of SAR data for marine applications. This letter focuses on the feature analysis of ships in high-resolution SAR images and proposes an improved optimizing algorithm for ship detection. A fast block detector is designed to extract sea clutter in a uniform local area, and then a constant false alarm rate detector is employed. Based on the kernel density estimation of ships, aspect ratio, and pixel points, ships are identified. TerraSAR-X and COSMO-SkyMed images are used to test our algorithm. The experimental results show that this algorithm can be implemented with time-saving, high-precision ship extraction, feature analysis, and detection.

In recent years, a lot of research has been conducted in the field of object detection for aerial image, and many effective methods have emerged. This paper enhances the existing Faster RCNN model to achieve better detection accuracy. We improve the feature extraction of Faster RCNN model using multi-scale feature fusion. Compared with the traditional Faster RCNN model, the accuracy on mAP is improved by 1.06% by applying the proposed method. The visual effects and numerical results of object detection verify the improvement of the proposed method.

Object detection in aerial images plays a crucial role across diverse domains such as agriculture, environmental monitoring, and security. Aerial images present several challenges, including dense small objects, intricate backgrounds, and occlusions, necessitating robust detection algorithms. This paper addresses the critical need for accurate and efficient object detection in aerial images using a Transformer-based approach enhanced with specialized methodologies, termed DFS-DETR. The core framework leverages RT-DETR-R18, integrating the Cross Stage Partial Reparam Dilation-wise Residual Module (CSP-RDRM) to optimize feature extraction. Additionally, the introduction of the Detail-Sensitive Pyramid Network (DSPN) enhances sensitivity to local features, complemented by the Dynamic Scale Sequence Feature-Fusion Module (DSSFFM) for comprehensive multi-scale information integration. Moreover, Multi-Attention Add (MAA) is utilized to refine feature processing, which enhances the model’s capacity for understanding and representation by integrating various attention mechanisms. To improve bounding box regression, the model employs MPDIoU with normalized Wasserstein distance, which accelerates convergence. Evaluation across the VisDrone2019, AI-TOD, and NWPU VHR-10 datasets demonstrates significant improvements in the mean average precision (mAP) values: 24.1%, 24.0%, and 65.0%, respectively, surpassing RT-DETR-R18 by 2.3%, 4.8%, and 7.0%, respectively. Furthermore, the proposed method achieves real-time inference speeds. This approach can be deployed on drones to perform real-time ground detection.

The use of Unmanned Aerial Vehicles (UAVs) is quite common today. Equipped with various tools, these vehicles are actively utilized in both civilian and military fields. Due to their high-altitude photography, images captured from UAVs often have a wide field of view. However, adverse weather conditions can reduce visibility, making it impossible for the operator controlling the UAV to see all the details. To analyze the images obtained from the UAV and improve environmental conditions, an automated system is needed. A different approach is required to mitigate the negative effects of weather and to detect small objects, which may appear smaller due to high-altitude captures. In this study, the adverse weather conditions faced by UAVs were identified, and subsequently, the Pix2Pix model was used to improve the results caused by these conditions. Enhancements were made in algorithms that were insufficient for detecting small objects. After correcting for adverse weather conditions, object recognition algorithms such as YOLOv8 and Faster R-CNN were used separately for ship detection and classification. It was found that after correcting for adverse weather conditions. there was an increase in mAP values of approximately 2% with YOLOv8 and 1% with Faster R-CNN.

For an autonomous ship to navigate safely and avoid collisions with other ships, reliably detecting and classifying nearby ships under various maritime meteorological environments is essential. In this paper, a novel probabilistic ship detection and classification system based on deep learning is proposed. The proposed method aims to detect and classify nearby ships from a sequence of images. The method considers the confidence of a deep learning detector as a probability; the probabilities from the consecutive images are combined over time by Bayesian fusion. The proposed ship detection system involves three steps. In the first step, ships are detected in each image using Faster region-based convolutional neural network (Faster R-CNN). In the second step, the detected ships are gathered over time and the missed ships are recovered using the Intersection over Union of the bounding boxes between consecutive frames. In the third step, the probabilities from the Faster R-CNN are combined over time and the classes of the ships are determined by Bayesian fusion. To train and evaluate the proposed system, we collected thousands of ship images from Google image search and created our own ship dataset. The proposed method was tested with the collected videos and the mean average precision increased by 89.38 to 93.92% in experimental results.

We reviewed the current key techniques of portable mass spectrometer and discussed its application in environmental monitoring. At present, mass spectrometer has two development directions. One direction is high-throughputs, high-sensitivity and high-resolution laboratory mass spectrometer. The other is small, in-situ monitoring portable mass spectrometer. With the rapid development of electronic technology and new materials, portable mass spectrometers are widely used in environmental pollution monitoring because they have the advantages of small size, light weight and low power consumption.