A performance comparison of YOLOv8 models for traffic sign detection in the Robotaxi-full scale autonomous vehicle competition

Soylu, Emel; Soylu, Tuncay

doi:10.1007/s11042-023-16451-1

Cited by 33 publications

(8 citation statements)

References 35 publications

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“…YOLOv5, a cutting-edge one-stage target recognition algorithm leveraging Convolutional Neural Networks (CNNs), stands out for its exceptional speed and accuracy in object detection [18] [19]. Originating in 2015 as YOLO under Joseph Redmon, the series evolved through versions 1 to 3, incorporating innovations like anchor boxes and feature pyramids.…”

Section: Model Overviewmentioning

confidence: 99%

Revolutionizing Plant Disease Detection in Agriculture: a Comparative Study of Yolov5 and Yolov8 Deep Learning Models

Hawaldar,

Jain,

Mengde

et al. 2024

Preprint

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Object detection stands as a pivotal task within computer vision, finding extensive use across various domains. Recent years have witnessed a transformative shift in object detection thanks to deep learning methodologies, with You Only Look Once(YOLO) emerging as a prominent algorithm in this field. In this research paper, our focus lies in conducting an in-depth comparative analysis between two advanced deep learning models, You Only Look Once Version 5(YOLOv5) and You Only Look Once Version 8 (YOLOv8), to assess their applicability in the context of plant leaf disease detection within the agricultural sector. Our results unequivocally establish YOLOv8 as the superior performer, exhibiting exceptional precision, recall, and class differentiation, and notably, outperforming YOLOv5 by approximately 3% in mean average precision (mAP). This study demonstrates the prowess of YOLOv8 as a state-of-the-art object detection algorithm, offering implications for diverse applications beyond agriculture.

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Section: Model Overviewmentioning

confidence: 99%

Revolutionizing Plant Disease Detection in Agriculture: a Comparative Study of Yolov5 and Yolov8 Deep Learning Models

Hawaldar,

Jain,

Mengde

et al. 2024

Preprint

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“…where T is the number of categories (segmented trees) and AP is the average precision of segmentation 42,44 ,…”

Section: Object Detection Model Using Convolutional Neural Networkmentioning

confidence: 99%

“…where P is the precision of identified objects, given by the number of true positives divided by the sum of true and false positives, and N is the number of samples. Finally, the intersection over Union IoU is a measure of how much the detection box overlaps a box containing the real object (ground truth) 44 :…”

Section: Object Detection Model Using Convolutional Neural Networkmentioning

confidence: 99%

Detection and tracking of barchan dunes using Artificial Intelligence

Cúñez,

Franklin

2024

Preprint

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Barchans are crescent-shape dunes ubiquitous on Earth and other celestial bodies, which are organized in barchan fields where they interact with each other. Over the last decades, satellite images have been largely employed to detect barchans on Earth and on the surface of Mars, with AI (Artificial Intelligence) becoming an important tool for monitoring those bedforms. However, automatic detection reported in previous works is limited to isolated dunes and does not identify successfully groups of interacting barchans. In this paper, we inquire into the automatic detection and tracking of barchans by carrying out experiments and exploring the acquired images using AI. After training a neural network with images from controlled experiments where complex interactions took place between dunes, we did the same for satellite images from Earth and Mars. We show, for the first time, that a neural network trained properly can identify and track barchans interacting with each other in different environments, using different image types (contrasts, colors, points of view, resolutions, etc.), with confidence scores (accuracy) above 70%. Our results represent a step further for automatically monitoring barchans, with important applications for human activities on Earth, Mars and other celestial bodies.

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“…Their core idea is to simultaneously perform object localization and classification in a single pass. Compared to two-stage methods, one-stage algorithms offer higher speed and real-time performance, making them well-suited for applications that require rapid responses, especially in road scene object detection [29,30].…”

Section: Research On One-stage Approaches In Object Detectionmentioning

confidence: 99%

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Liu,

Lin

2023

Electronics

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Intelligent traffic systems represent one of the crucial domains in today’s world, aiming to enhance traffic management efficiency and road safety. However, current intelligent traffic systems still face various challenges, particularly in the realm of target detection. These challenges include adapting to complex traffic scenarios and the lack of precise detection for multiple objects. To address these issues, we propose an innovative approach known as YOLOv8-SnakeVision. This method introduces Dynamic Snake Convolution, Context Aggregation Attention Mechanisms, and the Wise-IoU strategy within the YOLOv8 framework to enhance target detection performance. Dynamic Snake Convolution assists in accurately capturing complex object shapes and features, especially in cases of target occlusion or overlap. The Context Aggregation Attention Mechanisms allow the model to better focus on critical image regions and effectively integrate information, thus improving its ability to recognize obscured targets, small objects, and complex patterns. The Wise-IoU strategy combines dynamic non-monotonic focusing mechanisms, aiming to more precisely regress target bounding boxes, particularly for low-quality examples. We validate our approach on the BDD100K and NEXET datasets. Experimental results demonstrate that YOLOv8-SnakeVision excels in various complex road traffic scenarios. It not only enhances small object detection but also strengthens the ability to recognize multiple targets. This innovative method provides robust support for the development of intelligent traffic systems and holds the promise of achieving further breakthroughs in future applications.

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A performance comparison of YOLOv8 models for traffic sign detection in the Robotaxi-full scale autonomous vehicle competition

Cited by 33 publications

References 35 publications

Revolutionizing Plant Disease Detection in Agriculture: a Comparative Study of Yolov5 and Yolov8 Deep Learning Models

Revolutionizing Plant Disease Detection in Agriculture: a Comparative Study of Yolov5 and Yolov8 Deep Learning Models

Detection and tracking of barchan dunes using Artificial Intelligence

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

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