Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3

Chun, Liu; Li, Jian

doi:10.1155/2021/2889115

Cited by 8 publications

(9 citation statements)

References 16 publications

(17 reference statements)

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“…e loss during training was recorded and plotted as a loss curve and compared with the model used in [1]. As can be seen from Figure 5, the YOLOv3 model [23][24][25] used in this study has a lower loss value and is better able to achieve the recognition of industrial meter types.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

Zhang¹,

Chen²,

Lei³

2022

Mobile Information Systems

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Aiming at the demand of industrial instrument reading, this study proposes a method of industrial instrument classification and reading recognition based on YOLOv3. Given that industrial meters can be divided into pointer meters and digital meters according to the dial type, this method conducts a reading study for each of the two types of meters. Firstly, the YOLOv3 model is trained to recognize and detect the meter types and classify the meters according to the values of the obtained classes. The pointer meter uses a Hough circle to detect the dial, extracts the scale and the pointer, calculates the angle between the 0 scale line and the pointer, and obtains the reading of the pointer meter. The digital meter extracts the digits by finding the contours of the dial and the digit area and then uses a support vector machine (SVM) to identify the extracted digits and output the readings of the digital meter. Through the test, the mean average precision (mAP) of the recognition model in this study is 93.73%. The absolute error of pointer meter reading is less than 0.1 in general, and the maximum relative error is 0.35%. The accuracy of the digital meter reading is 99.7%. The proposed method can accurately read the value of the instrument and meet the needs of industrial production.

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Section: Experimental Results and Analysismentioning

confidence: 99%

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

Zhang¹,

Chen²,

Lei³

2022

Mobile Information Systems

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“…Chun Liu et al [21] proposed a model by adjusting and optimizing various parameters. Deep learning is used in conjunction with target HSV color histogram features and target's LBP local features.…”

Section: Methodsmentioning

confidence: 99%

Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Gadamsetty

Rupa

et al. 2022

Water

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Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof.

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“…YOLOv3 performs frame prediction in a clustering manner and uses the predicted 4 values as the parameters to determine the frame prediction. They are the horizontal and vertical axes of the center point of the frame and the width and height of the frame [17]. The four parameters work together to predict the frame, and the determination of the frame target is expressed by the confidence, and the value range of the confidence is ½0, 1.…”

Section: Moving Target Detection Technology Of Uav Visionmentioning

confidence: 99%

“…It can be seen from formula (17) that W k represents the 5 Wireless Communications and Mobile Computing gain, and the smaller the observation covariance R, the greater the gain. At the same time, the smaller the P − k , the larger the gain value.…”

Section: Kalman Filter Algorithmmentioning

confidence: 99%

Moving Target Detection Technology Based on UAV Vision

Cheng¹,

Qin²,

Chen³

et al. 2022

Wireless Communications and Mobile Computing

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The detection of moving objects by machine vision is a hot research direction in recent years. It is widely used in military, medical, transportation, and agriculture. With the rapid development of UAV technology, as well as the high mobility of UAVs and the wide range of high-altitude vision, the target detection technology based on UAV vision is applied to traffic management such as vehicle tracking and detection of vehicle violations. The moving target detection technology in this study is based on the YOLOv3 algorithm. It implements moving vehicle tracking by means of Mean-Shift and Kalman filtering. In this paper, the Gaussian background difference technology is used to analyze the illegal behavior of the vehicle, and the color feature extraction technology is used to identify and locate the license plate, and the information of the illegal vehicle is entered into the database. The experiment compares the moving target detection of UAV vision and the traditional target detection in four aspects: recognition accuracy, recognition speed, manual time, and divergent results. The results show that the average accuracy rates of UAV vision-based moving target detection and traditional pattern recognition are 98.4% and 87.8%, respectively. The recognition speeds are 24.9 (vehicles/sec) and 10.6 (vehicles/sec), respectively. However, the artificial time and divergence results of moving target detection based on UAV vision are only 1/3 of the traditional mode. The moving target detection based on UAV vision has a better moving target detection ability.

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Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3

Cited by 8 publications

References 16 publications

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Moving Target Detection Technology Based on UAV Vision

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