Ship Target Detection Based on Improved YOLO Network

Huang, Hong; Sun, Dechao; Wang, Renfang; Zhu, Chun; Liu, Bangquan

doi:10.1155/2020/6402149

Cited by 29 publications

(22 citation statements)

References 22 publications

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“…However, the error between the predicted value and real value is usually computed by three loss functions such as: classification loss, localization loss, and confidence loss. The final crossentropy loss function with the combination of three loss functions as follows [44]:…”

Section: A Yolov3 Algorithm Analysismentioning

confidence: 99%

A YOLOv3 Deep Neural Network Model to Detect Brain Tumor in Portable Electromagnetic Imaging System

et al. 2021

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This paper presents the detection of brain tumors through the YOLOv3 deep neural network model in a portable electromagnetic (EM) imaging system. YOLOv3 is a popular object detection model with high accuracy and improved computational speed. Initially, the scattering parameters are collected from the nineantenna array setup with a tissue-mimicking head phantom, where one antenna acts as a transmitter and the other eight antennas act as receivers. The images are then reconstructed from the post-processed scattering parameters by applying the modified delay-multiply-and-sum algorithm that contains 416×416 pixels. Fifty sample images are collected from the different head regions through the EM imaging system. The images are later augmented to generate a final image data set for training, validation, and testing, where the data set contains 1000 images, including fifty samples with a single and double tumor. 80% of the images are utilized for training the network, whereas 10% are used for validation, and the rest 10% are utilized for testing purposes. The detection performance is investigated with the different image data sets. The achieved detection accuracy and F1 scores are 95.62% and 94.50%, respectively, which ensure better detection accuracy. The training accuracy and validation losses are 96.74% and 9.21%, respectively. The tumor detection with its location in different cases from the testing images is evaluated through YOLOv3, which demonstrates its potential in the portable electromagnetic head imaging system.

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Section: A Yolov3 Algorithm Analysismentioning

confidence: 99%

A YOLOv3 Deep Neural Network Model to Detect Brain Tumor in Portable Electromagnetic Imaging System

et al. 2021

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“…Huang et al [80] used k-means++ clustering on the dimensions of bounding boxes to prioritize the model, improve the YOLOv3-Darnet53 network, increase jump connection mechanism, decrease feature redundancy, and improve the ability of tiny ship detection. On the premise of ensuring real-time performance, the precision of ship identification is improved by 12.5%, and the recall rate is increased by 11.5%.…”

Section: Maritime Surveillancementioning

confidence: 99%

Survey on Deep Learning-Based Marine Object Detection

Zhang

et al. 2021

Journal of Advanced Transportation

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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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“…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%

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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Ship Target Detection Based on Improved YOLO Network

Cited by 29 publications

References 22 publications

A YOLOv3 Deep Neural Network Model to Detect Brain Tumor in Portable Electromagnetic Imaging System

A YOLOv3 Deep Neural Network Model to Detect Brain Tumor in Portable Electromagnetic Imaging System

Survey on Deep Learning-Based Marine Object Detection

Modified Yolov3 for Ship Detection with Visible and Infrared Images

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