Computer Vision and Robotics

doi:10.1007/978-981-19-7892-0

Algorithms for Intelligent Systems

2023

DOI: 10.1007/978-981-19-7892-0

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Computer Vision and Robotics

Abstract: This paper focuses on the development of a multi-agent system in which several planning agents provide alternative plans for a given situation. This multi-agent system constitutes, at a higher abstraction level what is called the Task Planning Agent, an agent that belongs to a Multi-Agent Architecture used to control one single mobile robot.

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2024

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DepthCrackNet: A Deep Learning Model for Automatic Pavement Crack Detection

Saberironaghi,

Ren

2024

J. Imaging

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Detecting cracks in the pavement is a vital component of ensuring road safety. Since manual identification of these cracks can be time-consuming, an automated method is needed to speed up this process. However, creating such a system is challenging due to factors including crack variability, variations in pavement materials, and the occurrence of miscellaneous objects and anomalies on the pavement. Motivated by the latest progress in deep learning applied to computer vision, we propose an effective U-Net-shaped model named DepthCrackNet. Our model employs the Double Convolution Encoder (DCE), composed of a sequence of convolution layers, for robust feature extraction while keeping parameters optimally efficient. We have incorporated the TriInput Multi-Head Spatial Attention (TMSA) module into our model; in this module, each head operates independently, capturing various spatial relationships and boosting the extraction of rich contextual information. Furthermore, DepthCrackNet employs the Spatial Depth Enhancer (SDE) module, specifically designed to augment the feature extraction capabilities of our segmentation model. The performance of the DepthCrackNet was evaluated on two public crack datasets: Crack500 and DeepCrack. In our experimental studies, the network achieved mIoU scores of 77.0% and 83.9% with the Crack500 and DeepCrack datasets, respectively.

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DepthCrackNet: A Deep Learning Model for Automatic Pavement Crack Detection

Saberironaghi,

Ren

2024

J. Imaging

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Computer Vision and Robotics

Cited by 1 publication

References 7 publications

DepthCrackNet: A Deep Learning Model for Automatic Pavement Crack Detection

DepthCrackNet: A Deep Learning Model for Automatic Pavement Crack Detection

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