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Comparative Study on Distributed Lightweight Deep Learning Models for Road Pothole Detection.

Hassam Tahir¹, Eun-Sung Jung¹

¹Department of Software & Communications Engineering, Hongik University, Sejong 30016, Republic of Korea.

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|May 13, 2023

Summary

This summary is machine-generated.

This study enhances distributed deep learning for intelligent traffic systems. A hybrid pipeline using PyTorch improved image detection accuracy and reliability on edge devices.

Keywords:

distributed deep-learning distributed edge AI/ML distributed hybrid model training

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Deep learning models for intelligent traffic systems often struggle with accuracy on edge devices due to limited data.
Existing distributed training techniques face communication constraints and speed limitations.

Purpose of the Study:

To develop and evaluate a novel hybrid distributed deep-learning pipeline for improved image detection in intelligent traffic systems.
To address accuracy and reliability issues in neural networks deployed on edge devices.

Main Methods:

Implemented and compared distributed data and model parallel techniques using TensorFlow and PyTorch.
Developed a hybrid pipeline combining dataset and model distribution with an all-reduced algorithm and NVlinks.
Evaluated performance on edge and Google cluster environments using metrics like training time, images/second, and loss.

Main Results:

The proposed hybrid pipeline demonstrated superior performance over other tested settings.
PyTorch's hybrid pipeline achieved better results compared to TensorFlow.
Bounding box detection quality met expectations with enhanced reliability.

Conclusions:

The hybrid pipeline effectively overcomes communication constraints and improves the speed and accuracy of distributed deep learning for traffic image detection.
PyTorch's implementation of the hybrid approach shows significant advantages for intelligent traffic systems and self-driving cars.