Automatic Pothole and Humps on Roads Detection and Notification Alert

Bhavana, Kunapareddy; Munappa, Sunitha; Bhavani, Kondapalli Divya; Deshmanth, Ponaganti; Swathi, Angadi; Vanga, Siva Reddy

doi:10.1109/icears56392.2023.10085086

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…The model's performance is assessed by measuring precision (P), recall (R), and mean average precision (mAP), while frames per second (FPS) is utilized as an indicator of speed [8][9] . To assess the efficiency of different enhancement strategies and the suggested algorithm, a set of tests were carried out in a consistent experimental setting with the same parameter configurations [10] .…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Steel surface defect detection based on deformable convolution feature fusion

Wang,

Yin,

Zhang

2024

Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024)

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In steel production, the quantity of surface defects is a key indicator. Traditional detection methods mainly use flash frequency to detect defects, but this method has issues such as slow speed and low accuracy. Therefore, it is of great research significance to improve the detection speed and accuracy of surface defects in steel. The main focus of this paper is to explore the task of detecting surface defects in steel, and we will first improve the YOLOv8 model to enhance its performance in detecting surface defects on steel. We will optimize the loss function calculation method, neck end, and backbone network. The specific improvements are as follows: To address issues such as dense distribution and easy overlap of annotation boxes for steel surface defect targets, we introduce deformable convolution modules into the backbone network of YOLOv8. By adding deformable convolution modules, we can flexibly handle situations where detection point receptive fields are insufficient and enhance attention towards detection targets. Secondly, replacing PANet feature pyramid with BiFPN, network allows for more effective capture of target boundary information and improves detection efficiency through multi-scale semantic feature fusion. Finally, in order to accelerate model convergence speed and improve regression accuracy when improving the YOLOv8 model, we decide to replace the CIoU regression loss function used in the original model with EIoU(Efficient Intersection over Union) loss function.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Steel surface defect detection based on deformable convolution feature fusion

Wang,

Yin,

Zhang

2024

Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024)

View full text Add to dashboard Cite

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Strengthening Automotive Cybersecurity: A Comparative Analysis of ISO/SAE 21434-Compliant Automatic Collision Notification (ACN) Systems

Boi,

Gupta,

Rinhel

et al. 2023

Vehicles

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The increasing usage of autonomous and automatic systems within the automotive industry is steering us towards a more interconnected world. This enhanced interconnectivity fosters a more streamlined driving experience, reduces costs, and provides timely driver assistance. The electric/electronic (EE) architectures of modern vehicles are inherently complex due to the multitude of components they encompass. Contemporary architectures reveal that these components converge at an electronic control unit (ECU) called the central gateway, which could potentially represent a single point of failure. While this central unit is typically adequately safeguarded, the same cannot be said for the connected components, which often remain vulnerable to cyber threats. The ISO/SAE 21434 standard paved the way for automotive cybersecurity and could be used in parallel with other standards such as ISO 26262 and ISO PAS 21488. Automatic collision notification (ACN) is one of the most typical systems in a vehicle, and limited effort has been dedicated to identifying the most suitable architecture for this feature. This paper addresses the existing security and privacy gap of this feature by conducting a comparative analysis of security threats in two distinct ACN architectures. Notably, despite ACN architectures exhibiting inherent similarities, the primary distinction between the two architectures lies in their strategies for crash estimation and detection, followed by subsequent communication with emergency response teams. A rigorous security assessment was conducted using the ISO/SAE 21434 standard, employing the TARA and STRIDE methodologies through the Ansys medini analyze software. This analysis identified an average of 310 threats per architecture, including a significant number of high-level threats (11.8% and 15%, respectively), highlighting the importance of a comprehensive evaluation.

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Automatic Pothole and Humps on Roads Detection and Notification Alert

Cited by 2 publications

References 12 publications

Steel surface defect detection based on deformable convolution feature fusion

Steel surface defect detection based on deformable convolution feature fusion

Strengthening Automotive Cybersecurity: A Comparative Analysis of ISO/SAE 21434-Compliant Automatic Collision Notification (ACN) Systems

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