Marine Target Detection Based on Improved Faster R-CNN for Navigation Radar PPI Images

Mou, Xiaoqian; Chen, Xiaolong; Guan, Jian; Chen, Baoxin; Dong, Yanfang

doi:10.1109/iccais46528.2019.9074588

Cited by 31 publications

(15 citation statements)

References 6 publications

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“…This network is composed of the fully convolutional network (FCN), the region proposal network (RPN), and the region of interest (ROI) subnetwork. FCN is used for feature extraction of input original image to generate feature map, RPN generates regions of interest (ROI) [32,33] according to the extracted features, and ROI subnet locates and classifies target areas according to features extracted by FCN and ROI output by RPN. R-FCN first uses FCN to convert the original image into a corresponding feature map and then uses RPN to filter the foreground information on the feature map and frame the area that belongs to the object.…”

Section: Proposed Methodsmentioning

confidence: 99%

Mutual Positioning Method in Unknown Indoor Environment Based on Visual Image Semantics

Wang

2021

Wireless Communications and Mobile Computing

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In our society, realizing intelligent positioning in indoor environments is important to build a smart city. Currently, mutual positioning requirements in the unknown indoor environment are growing fast. However, in such environment, we can obtain neither outdoor radio signal nor the indoor images in advance for online positioning. Therefore, how to achieve mutual positioning becomes an interesting problem. In this paper, we propose a vision-based mutual positioning method in an unknown indoor environment. First, two users take images of the unknown indoor environment, use semantic segmentation network to identify the semantic targets contained in the images, and upload the generated semantic sequence to the user shared database in real time. Then, every time two users reupload a semantic sequence due to a change of location, it is necessary to retrieve whether another user has uploaded the same semantic sequence in the shared database. If the retrieval is successful, it means that two users have seen the same scene. Finally, two users select a target from the two user images taken based on the same scene to establish a three-dimensional coordinate system, respectively, calculate their own position coordinates in this coordinate system, and realize mutual positioning through position coordinate sharing. Experiment results show that our proposed method can successfully realize mutual positioning between two users in an unknown indoor environment, while ensuring high positioning accuracy.

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Section: Proposed Methodsmentioning

confidence: 99%

Mutual Positioning Method in Unknown Indoor Environment Based on Visual Image Semantics

Wang

2021

Wireless Communications and Mobile Computing

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“…With the development of artificial intelligence in the fields of communication and radar, reinforcement learning algorithms can be introduced. In the fields of radar and communication, modulation identification, signal detection, radar target identification [20][21][22][23][24], cluster cooperative control [25][26][27][28][29][30] and resource allocation [31][32][33] are included. Additionally, policy-based reinforcement learning and action-critical deep deterministic policy gradient algorithms are used to allocate the energy resources of cellular network reasonably [34], and the resource allocation method for radar detection [35] is given.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System

et al. 2022

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Due to the limited ability of a single unmanned aerial vehicle (UAV), group unmanned aerial vehicles (UAVs) have attracted more attention in communication and radar fields. The use of an integrated sensing and communication (ISAC) system can make communication and radar modules share a radar module’s resources, coupled with efficient resource allocation methods. It can effectively solve the problem of inadequate UAV resources and the low utilization rate of resources. In this paper, the resource allocation problem is addressed for group UAVs to achieve a trade-off between the detection and communication performance, where the ISAC system is equipped in group UAVs. The resource allocation problem is described by an optimization problem, but with group UAVs, the problem is complex and cannot be solved efficiently. Compared with the traditional resource allocation scheme, which needs a lot of calculation or sample set problems, a novel reinforcement-learning-based method is proposed. We formulate a new reward function by combining mutual information (MI) and the communication rate (CR). The MI describes the radar detection performance, and the CR is for wireless communication. Simulation results show that compared with the traditional Kuhn Munkres (KM) or the deep neural network (DNN) methods, this method has better performance with the increase in problem complexity. Additionally, the execution time of this scheme is close to that of the DNN scheme, and it is better than the KM algorithm.

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“…Then, the advantages and disadvantages of these algorithms are analyzed. Aiming at the characteristics that the data set contains small targets and the resolution of the coastal defense radar is lower than that of the SAR image [18,19], we make some improvements based on the original Faster R-CNN, which help to improve the detection accuracy. The main contributions are listed as follows in more detail:…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

Chen

Jin

et al. 2021

Remote Sensing

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The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

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Marine Target Detection Based on Improved Faster R-CNN for Navigation Radar PPI Images

Cited by 31 publications

References 6 publications

Mutual Positioning Method in Unknown Indoor Environment Based on Visual Image Semantics

Mutual Positioning Method in Unknown Indoor Environment Based on Visual Image Semantics

Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System

Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

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