Multi-Target Instance Segmentation and Tracking Using YOLOV8 and BoT-SORT for Video SAR

Yan, Shangqu; Fu, Yaowen; Zhang, Wenpeng; Yang, Wei; Yu, Ruofeng; Zhang, Fatong

doi:10.1109/eei59236.2023.10212903

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…Despite a slight reduction in detection speed, this model exhibits the highest mean average precision, for instance, segmentation. This is particularly evident in the intersect over union (IoU) metrics at both 50% and 50-90%, as established during the model's development [38]. The main parts of this model are the head, neck, and backbone.…”

Section: D Training Modelmentioning

confidence: 91%

Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall

Nasimi,

Wood

2024

Remote Sensing

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Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deep learning-based automated detection and advanced image processing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method’s effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation.

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Section: D Training Modelmentioning

confidence: 91%

Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall

Nasimi,

Wood

2024

Remote Sensing

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“…However, when the target is partially or completely occluded, Kalman filtering [20] may not be able to accurately estimate the target's position and the tracking effect is poor.The SORT algorithm is susceptible to the problem of target ID confusion in multi-target tracking which means that different targets are incorrectly assigned the same ID. To solve such problems, Bot SORT [21] is an online and real-time target tracking algorithm. It is based on an improved version of the SORT algorithm, which can better deal with the problem of target re-identification by introducing appearance features.Even if the target is not visible for a period of time, it is still able to be matched and tracked by the appearance features,and target matching and ID assignment can be performed more accurately.…”

Section: Related Workmentioning

confidence: 99%

“…The Input input is mainly composed of Mosaic data enhancement, automatic image cropping and splicing, and adaptive anchor frames [21][22][23] .Mosaic data enhancement is used to increase the diversity of the training data by splicing multiple images together to form a single large input image.In this way, the model can be made to learn the features of the target better at different locations and scales. Automatic image cropping stitching is used to generate a larger input image by automatically cropping and stitching images.…”

Section: Introduction To the Yolov8n Algorithmmentioning

confidence: 99%

Based on improved YOLOv8 and Bot SORT surveillance video traffic statistics

Yang,

Pi,

Wang

et al. 2024

Preprint

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Aiming at the problems of leakage detection and low detection accuracy of existing deep learning based surveillance video traffic flow detection algorithms, a traffic flow counting system combining improved YOLOv8 detection and Bot SORT tracking is proposed. First, the backbone network is used to incorporate the SPD-Conv convolutional layer to improve the network's ability to detect small targets. Then, the attention mechanism CoTAttention is introduced into the neck network to further improve the model generalization ability. Finally, the improved YOLOv8 model and the Bot SORT algorithm are combined to design and implement a traffic counting system capable of monitoring video traffic in real time, and trained and tested on the open-source UA-DETRAC vehicle detection dataset. The experimental results show that the improved YOLOv8 algorithm improves F1, P, mAP50, and mAP50-95 by 0.36, 2.2, 1.8, and 2.1 percentage points, respectively, compared with the original algorithm. Combined with the Bot SORT tracking, it achieves more accurate and reliable results in the task of traffic counting, which provides a strong support for the vehicle detection and counting in the monitoring system.

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Monocular Visual Navigation Algorithm for Nursing Robots via Deep Learning Oriented to Dynamic Object Goal

Fu,

Wang,

Yang

et al. 2023

J Intell Robot Syst

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Multi-Target Instance Segmentation and Tracking Using YOLOV8 and BoT-SORT for Video SAR

Cited by 12 publications

References 16 publications

Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall

Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall

Based on improved YOLOv8 and Bot SORT surveillance video traffic statistics

Monocular Visual Navigation Algorithm for Nursing Robots via Deep Learning Oriented to Dynamic Object Goal

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