Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

Roy, Koushik; Chaudhuri, Sheli Sinha; Pramanik, Sayan; Banerjee, Soumen

doi:10.32604/csse.2023.024997

Cited by 6 publications

(2 citation statements)

References 42 publications

(46 reference statements)

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“…Ship network security as the whole rear guarantee, the ship must always be in an absolute state of safety to ensure its normal operation [23,24]. For example, maritime container transport ships attach great importance to the security protection of the ship network system due to the relatively high value of their cargo ships.…”

Section: Experiments Of Real-time Detection and Protection Of The Int...mentioning

confidence: 99%

Real-Time Risk Detection Method and Protection Strategy for Intelligent Ship Network Security Based on Cloud Computing

Guo

2023

Symmetry

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When studying an unfamiliar system, we first look for the symmetry that the system has, so that we can make many predictions about the possible properties of the system. The symmetry in ship network security needs to maintain a stable state and maintain a constant state of ship network security. With the rapid development of network information technology, smart ships have become a new hot spot in the international shipping industry. The smart ships cybersecurity discussion is also at the top of the list in the maritime field. More and more shipping companies feel that their smart ship systems need to be upgraded and the main reason behind this is that the systems are maliciously attacked by cyber hackers. Therefore, it is extremely important to detect and protect the security of intelligent ship network systems in real time. The issue of network security has always accompanied the whole process of the development of the Internet. At the same time, with the development of Internet technology, network hacking attacks against the Internet have never stopped developing, and traditional ship network security risk detection and protection cannot achieve good results. After understanding the operation mode of intelligent ship networks, this paper deeply studied the characteristics of cloud computing technology and proposed a real-time risk detection method and protection strategy for intelligent ship network security based on cloud computing. This paper mainly used multi-sensor nodes to analyze data containing malicious attack information and implemented self-execution protection strategy generation nodes to intercept and protect from the attack, so as to achieve the purpose of maintaining the network security of intelligent ships. Through experiments, the virus intrusion detection and defense rate of the algorithm proposed in this paper was able to reach 85% to 95%, while the virus intrusion detection defense rate of the traditional intelligent ship network security protection algorithm was 55% to 65%. The detection rate of the algorithm proposed in this paper was able to reach 96.95% and the false positive rate was 2.56%. The detection rate of the traditional algorithm was only 70.76%, while the false positive rate reached 4.69%. All of the proposed algorithm’s data were significantly better than that of traditional algorithms, which proved that the performance of cloud computing-based real-time risk detection and protection algorithms for intelligent ship network security was significantly better than that of traditional algorithms.

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Section: Experiments Of Real-time Detection and Protection Of The Int...mentioning

confidence: 99%

Real-Time Risk Detection Method and Protection Strategy for Intelligent Ship Network Security Based on Cloud Computing

Guo

2023

Symmetry

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“…The ML applications belong to the artificial intelligence (AI) methods which can learn and develop the system from the experiences without a clear program. Its applications mainly concentrate on the improvement of computer programs that can able to obtain data and employ it to absorb personally [7]. During the last year, deep learning (DL) particularly in the area of computer vision (CV) has attained wonderful achievement and become the fundamental solution for object recognition and image identification [8].…”

Section: Introductionmentioning

confidence: 99%

Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep Learning

Alghamdi,

Mengash,

Aljebreen

et al. 2024

IEEE Access

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Recently, consumer product recognition comprises harnessing cutting-edge technologies namely artificial intelligence (AI) and computer vision (CV) to develop the purchasing expedition. This technology allows retailers to utilize robust product recognition systems that precisely identify and categorize products in real time. The comprehension of automatic product identification becomes of major importance for both social and economic improvement since it is more reliable and time-consuming than manual function. Product detection through images is a complex task in the domain of CV. This can be obtained the improving consideration because of the excellent application viewpoint like visually impaired assistance, stock tracking, automatic checkout, and planogram compliance. Currently, deep learning (DL) prefers a successful progression with great achievements in object detection and image classification. Therefore, this study presents Advanced Consumer Product Recognition using the Aquila Optimization Algorithm with Deep Learning (ACPR-AOADL) technique. The proposed ACPR-AOADL model utilizes hyperparameter-tuned DL concepts for the identification of consumer products. To achieve this, the ACPR-AOADL model first pre-processes the input data utilizing a Wiener filter (WF) to improve the image quality. Besides, the YOLO-v8 model with a deep residual network (DRN) as a backbone network can be applied for the product detection process. For product classification, the deep belief network (DBN) approach can be used. To boost the complete product detection process, the ACPR-AOADL technique involves AOA based hyperparameter selection process. The performance analysis of the ACPR-AOADL method can be examined under the Product-10K dataset. Wide-ranging results stated that the ACPR-AOADL technique reaches enhanced classification performance over other compared approaches.

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An intelligence enhancement method for USV navigation visual measurement based on variable gradient soft-threshold correction

Shi,

Cao,

Han

et al. 2025

Measurement

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Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

Cited by 6 publications

References 42 publications

Real-Time Risk Detection Method and Protection Strategy for Intelligent Ship Network Security Based on Cloud Computing

Real-Time Risk Detection Method and Protection Strategy for Intelligent Ship Network Security Based on Cloud Computing

Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep Learning

An intelligence enhancement method for USV navigation visual measurement based on variable gradient soft-threshold correction

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