Lightweight Ship Detection Methods Based on YOLOv3 and DenseNet

Li, Zhelin; Zhao, Lining; Han, Xu; Pan, Mingyang; Hwang, Feng-Jang

doi:10.1155/2020/4813183

Cited by 25 publications

(16 citation statements)

References 34 publications

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“…The lack of both visible and infrared images required to perform all-day ship detection in common datasets must be addressed. Therefore, the results of previous studies [22][23][24][25] cannot be compared because every approach utilizes a different dataset and no common basis for comparison is established. In order to compare the effectiveness of the proposed method with other ship detection methods, this study first constructed a ship data set composed of 5027 visible and 530 infrared images.…”

Section: Ship Datasetmentioning

confidence: 99%

“…Recently, computer vision based on deep learning and convolutional neural networks (CNNs) have been widely used in various fields, especially for object detection and classification. Semantic image features extracted by the deep CNNs (DCNNs) are robust to morphological changes, image noise, and relative object positions in visual images [22][23][24][25]. Therefore, this research was motivated to utilize an efficient deep learning network to achieve automatic feature extraction for machine learning.…”

Section: Introductionmentioning

confidence: 99%

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Modified Yolov3 for Ship Detection with Visible and Infrared Images

et al. 2022

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As the demands for international marine transportation increase rapidly, effective port management has become an important issue. Automatic ship recognition can facilitate the realization of smart ports, and improve the efficiency of port operation and management. In order to take into account the processing efficiency and detection accuracy at the same time, the study presented an improved deep-learning network based on You only look once version 3 (Yolov3) for all-day ship detection with visible and infrared images. Yolov3 network can simultaneously improve the recognition ability of large and small objects through multiscale feature-extraction architecture. Considering reducing computational time and network complexity with relatively competitive detection accuracy, the study modified the architecture of Yolov3 by choosing an appropriate input image size, fewer convolution filters, and detection scales. In addition, the reduced Yolov3 was further modified with the spatial pyramid pooling (SPP) module to improve the network performance in feature extraction. Therefore, the proposed modified network can achieve the purpose of multi-scale, multi-type, and multi-resolution ship detection. In the study, a common self-built data set was introduced, aiming to conduct all-day and real-time ship detection. The data set included a total of 5557 infrared and visible light images from six common ship types in northern Taiwan ports. The experimental results on the data set showed that the proposed modified network architecture achieved acceptable performance in ship detection, with the mean average precision (mAP) of 93.2%, processing 104 frames per second (FPS), and 29.2 billion floating point operations (BFLOPs). Compared with the original Yolov3, the proposed method can increase mAP and FPS by about 5.8% and 8%, respectively, while reducing BFLOPs by about 47.5%. Furthermore, the computational efficiency and detection performance of the proposed approach have been verified in the comparative experiments with some existing convolutional neural networks (CNNs). In conclusion, the proposed method can achieve high detection accuracy with lower computational costs compared to other networks.

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Section: Ship Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modified Yolov3 for Ship Detection with Visible and Infrared Images

et al. 2022

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“…It has the ability to output the identities of the objects, bounding boxes around them, as well as confidence values. It uses two networks (Li et al, 2020), a backbone network called "Darknet-53" with 53 convolutional layers, and a detection network. YOLO has several advantages.…”

Section: Detectnet V2mentioning

confidence: 99%

Effective real-time face mask detection on NVIDIA edge devices

Kam¹,

Lui²,

Du³

et al. 2022

HKIE Transactions

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During these difficult times of COVID-19, people are struggling to return to their normal routines, including going back to schools and workspaces. To prevent the spread of the disease, wearing face masks is essential for everyone to protect themselves and the ones around them. However, challenges arise in regard to enforcement of wearing masks in large crowds such as at educational centres and public transportation. This paper proposes a robust automatic system for face mask detection using transfer learning kits from NVIDIA. Based on the backbone of Resnet-18, the model results in high accuracy in the distinguishing of persons who do and do not wear masks. Leveraged by the NVIDIA edge accelerator, the system can run in real-time environments, making it applicable in various venues. Its feasibility was demonstrated by deploying the approach in an education centre in Hong Kong.

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“…It shows that the proposed method identifies the historical behavior of the detected object successfully, helps managers understand the historical navigation, predicts the future navigation trajectory, implements early warning measures to ensure maritime traffic safety. Li et al [124] proposed a lightweight ship detection model (LSDM) based on YOLOv3 and DenseNet, in which the backbone network is improved by using dense connection inspired from DenseNet, and the feature pyramid networks are improved by using spatial separation convolution to replace the original convolution network. In the proposed model, only one-third of the parameters of the YOLOv3 network can reach average accuracy of 94% for ship detection, and in the LSDM tiny network, just one-eighth of the parameters of the YOLOv3 network can reach double detection speed and average accuracy of 93.5%.…”

Section: Surface Moving Object Detectionmentioning

confidence: 99%

Survey on Deep Learning-Based Marine Object Detection

Zhang

et al. 2021

Journal of Advanced Transportation

Self Cite

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We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.

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Lightweight Ship Detection Methods Based on YOLOv3 and DenseNet

Cited by 25 publications

References 34 publications

Modified Yolov3 for Ship Detection with Visible and Infrared Images

Modified Yolov3 for Ship Detection with Visible and Infrared Images

Effective real-time face mask detection on NVIDIA edge devices

Survey on Deep Learning-Based Marine Object Detection

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