A Novel Target Detection Method of the Unmanned Surface Vehicle under All-Weather Conditions with an Improved YOLOV3

Li, Yan; Guo, Jianfeng; Guo, Xiaomin; Liu, Kaizhou; Zhao, Wentao; Luo, Yeteng; Wang, Zhenyu

doi:10.3390/s20174885

Cited by 32 publications

(22 citation statements)

References 28 publications

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“…It usually includes detection accuracy and average time cost aspects. For the detection accuracy, precision and recall analysis are utilized to measure it [25,33]. The precision and recall are defined as follows:…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…It usually includes detection accuracy and average time cost aspects. For the detection accuracy, precision and recall analysis are utilized to measure it [25,33] where, True Positives is the number of targets correctly identified, False Positives is the number of non-targets identified as targets and False Negatives is the number of nontargets identified as non-targets. Therefore, the high precision value means the detection results contain a high percentage of useful information and a low percentage of false alarms.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…Moreover, the learning capability of the detection and classification model is limited for the features of the rare taxa data if the amount of rare taxa data is relatively small. On the other hand, the structure of the DenseNet model [24] with the advantage of the feature reuse ability was fused into other detection models, such as YOLOV3, which reduced the feature loss in the deep neural network model and was proved effective for subtle feature retention [25,26].…”

Section: Introductionmentioning

confidence: 99%

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Plankton Detection with Adversarial Learning and a Densely Connected Deep Learning Model for Class Imbalanced Distribution

Guo

et al. 2021

JMSE

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Detecting and classifying the plankton in situ to analyze the population diversity and abundance is fundamental for the understanding of marine planktonic ecosystem. However, the features of plankton are subtle, and the distribution of different plankton taxa is extremely imbalanced in the real marine environment, both of which limit the detection and classification performance of them while implementing the advanced recognition models, especially for the rare taxa. In this paper, a novel plankton detection strategy is proposed combining with a cycle-consistent adversarial network and a densely connected YOLOV3 model, which not only solves the class imbalanced distribution problem of plankton by augmenting data volume for the rare taxa but also reduces the loss of the features in the plankton detection neural network. The mAP of the proposed plankton detection strategy achieved 97.21% and 97.14%, respectively, under two experimental datasets with a difference in the number of rare taxa, which demonstrated the superior performance of plankton detection comparing with other state-of-the-art models. Especially for the rare taxa, the detection accuracy for each rare taxa is improved by about 4.02% on average under the two experimental datasets. Furthermore, the proposed strategy may have the potential to be deployed into an autonomous underwater vehicle for mobile plankton ecosystem observation.

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Section: Performance Evaluation Metricsmentioning

confidence: 99%

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Plankton Detection with Adversarial Learning and a Densely Connected Deep Learning Model for Class Imbalanced Distribution

Guo

et al. 2021

JMSE

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“…and the "probe vehicle data" systems (i.e., floating car data FCD) both widely used (Table 1). In fixed spot measurement methods, for the vehicle detection and counting processes can be applied several algorithms based on Deep Learning [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and YOLOv3 Systems for Automatic Traffic Data Measurement by Moving Car Observer Technique

Guerrieri

Parla

2021

Infrastructures

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Macroscopic traffic flow variables estimation is of fundamental interest in the planning, designing and controlling of highway facilities. This article presents a novel automatic traffic data acquirement method, called MOM-DL, based on the moving observer method (MOM), deep learning and YOLOv3 algorithm. The proposed method is able to automatically detect vehicles in a traffic stream and estimate the traffic variables flow q, space mean speed vs. and vehicle density k for highways in stationary and homogeneous traffic conditions. The first application of the MOM-DL technique concerns a segment of an Italian highway. In the experiments, a survey vehicle equipped with a camera has been used. Using deep learning and YOLOv3 the vehicles detection and the counting processes have been carried out for the analyzed highway segment. The traffic flow variables have been calculated by the Wardrop relationships. The first results demonstrate that the MOM and MOM-DL methods are in good agreement with each other despite some errors arising with MOM-DL during the vehicle detection step due to a variety of reasons. However, the values of macroscopic traffic variables estimated by means of the Drakes’ traffic flow model together with the proposed method (MOM-DL) are very close to those obtained by the traditional one (MOM), being the maximum percentage variation less than 3%.

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“…The more complex feature fusion structure will improve accuracy, but it will also reduce detection speed and increase network complexity [ 27 , 28 ]. Based on YOLO v3, Li et al introduced the DenseNet structure to enhance the ability of YOLO v3 to extract features and conducted an experiment in the dataset of unmanned sea-surface vehicles [ 29 ]. Dai et al presented a two-stage detector to detect small targets in SAR images.…”

Section: Introductionmentioning

confidence: 99%

Study on Visual Detection Algorithm of Sea Surface Targets Based on Improved YOLOv3

Liu

Pang

et al. 2020

Sensors

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Countries around the world have paid increasing attention to the issue of marine security, and sea target detection is a key task to ensure marine safety. Therefore, it is of great significance to propose an efficient and accurate sea-surface target detection algorithm. The anchor-setting method of the traditional YOLO v3 only uses the degree of overlap between the anchor and the ground-truth box as the standard. As a result, the information of some feature maps cannot be used, and the required accuracy of target detection is hard to achieve in a complex sea environment. Therefore, two new anchor-setting methods for the visual detection of sea targets were proposed in this paper: the average method and the select-all method. In addition, cross PANet, a feature fusion structure for cross-feature maps was developed and was used to obtain a better baseline cross YOLO v3, where different anchor-setting methods were combined with a focal loss for experimental comparison in the datasets of sea buoys and existing sea ships, SeaBuoys and SeaShips, respectively. The results showed that the method proposed in this paper could significantly improve the accuracy of YOLO v3 in detecting sea-surface targets, and the highest value of mAP in the two datasets is 98.37% and 90.58%, respectively.

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A Novel Target Detection Method of the Unmanned Surface Vehicle under All-Weather Conditions with an Improved YOLOV3

Cited by 32 publications

References 28 publications

Plankton Detection with Adversarial Learning and a Densely Connected Deep Learning Model for Class Imbalanced Distribution

Plankton Detection with Adversarial Learning and a Densely Connected Deep Learning Model for Class Imbalanced Distribution

Deep Learning and YOLOv3 Systems for Automatic Traffic Data Measurement by Moving Car Observer Technique

Study on Visual Detection Algorithm of Sea Surface Targets Based on Improved YOLOv3

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