Maritime Target Detection Of Intelligent Ship Based On Faster R-CNN

Zou, Junjie; Yuan, Wei; Yu, Miao

doi:10.1109/cac48633.2019.8996260

Cited by 9 publications

(3 citation statements)

References 7 publications

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“…Finally, 2,700 images are used for training, and the accuracy rate of their model reaches 91.04%. Zou et al (2019) improved a maritime object detection method based on Faster R-CNN. The ResNet-50 network is replaced by the VGG16 network.…”

Section: Visual Object Detection Based On Deep Learningmentioning

confidence: 99%

Lightweight object detection algorithm based on YOLOv5 for unmanned surface vehicles

Zhang

Jin

et al. 2023

Front. Mar. Sci.

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Visual detection technology is essential for an unmanned surface vehicle (USV) to perceive the surrounding environment; it can determine the spatial position and category of the object, which provides important environmental information for path planning and collision prevention of the USV. During a close-in reconnaissance mission, it is necessary for a USV to swiftly navigate in a complex maritime environment. Therefore, an object detection algorithm used in USVs should have high detection s peed and accuracy. In this paper, a YOLOv5 lightweight object detection algorithm using a Ghost module and Transformer is proposed for USVs. Firstly, in the backbone network, the original convolution operation in YOLOv5 is upgraded by convolution stacking with depth-wise convolution in the Ghost module. Secondly, to exalt feature extraction without deepening the network depth, we propose integrating the Transformer at the end of the backbone network and Feature Pyramid Network structure in the YOLOv5, which can improve the ability of feature expression. Lastly, the proposed algorithm and six other deep learning algorithms were tested on ship datasets. The results show that the average accuracy of the proposed algorithm is higher than that of the other six algorithms. In particular, in comparison with the original YOLOv5 model, the model size of the proposed algorithm is reduced to 12.24 M, the frames per second reached 138, the detection accuracy was improved by 1.3%, and the mean of average precision (0.5) reached 96.6% (from 95.3%). In the verification experiment, the proposed algorithm was tested on the ship video collected by the “JiuHang 750” USV under different marine environments. The test results show that the proposed algorithm has a significantly improved detection accuracy compared with other lightweight detection algorithms.

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Section: Visual Object Detection Based On Deep Learningmentioning

confidence: 99%

Lightweight object detection algorithm based on YOLOv5 for unmanned surface vehicles

Zhang

Jin

et al. 2023

Front. Mar. Sci.

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“…Furthermore, SAR is effective to capture images of large ships but is unable to capture images of small vessels. Thus, recent ship recognition systems [4,5,7,8] employ cameras to capture images of ships and further adopts the computer vision technique [9,10] to implement ship recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Although ship recognition systems based on the computer vision technique have an advantage in deployment cost and small target recognition, they still provide coarse-grained services. In other words, prior ship recognition systems [4][5][6][7][8] only support ship target detection and classification, which fails to satisfy the increasing requirements of IWTS, such as tracking a given ship. Ship tracking provides crucial on-site microscopic kinematic traffic information which benefits traffic flow analysis, ship safety enhancement, traffic control, etc.…”

Section: Introductionmentioning

confidence: 99%

FGSR: A Fine-Grained Ship Retrieval Dataset and Method in Smart Cities

Xian

Jin

et al. 2022

Wireless Communications and Mobile Computing

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Ship reidentification is an important part of water transportation systems in smart cities. Existing ship reidentification methods lack a large-scale fine-grained ship retrieval dataset in the wild and existing ship recognition solutions mainly focus on the ship target identification rather than the fine-grained ship reidentification. Furthermore, previous ship target identification systems are usually based on synthetic aperture radar (SAR) image, automatic identification system (AIS) data, or video streaming, which is confronted with expensive deployment costs, such as the installation cost of SAR and AIS, and the communication and storage overhead. Indeed, ship reidentification benefits for traffic monitoring, navigation safety, vessel tracking, etc. To address these problems, we propose a new large-scale fine-grained ship retrieval dataset (named FGSR) that consists of 30,000 images of 1000 ships captured in the wild. Besides, to tackle the difficulty of spatial-temporal inconsistency in ship identification in the wild, we design a multioriented ship reidentification network named FGSR-Net that consists of three modules to address different crucial problems. The pyramid fusion module was aimed at addressing the problem of variant size and shape of ship targets, the occlusion modules attempt to detect the unchangeable area of ship images, while the multibranch identity module generates discriminative feature representation for ship targets from different orientations. Experimental evaluations on FGSR dataset show the effectiveness and efficiency of our proposed FGSR-Net. The mean average precision of ship reidentification is around 92.4%, and our FGSR-Net proposed method only takes 3 seconds to give the retrieval results from 30,000 images.

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