Deep Learning Approaches for Vehicle and Pedestrian Detection in Adverse Weather-Reference-Cited by-同舟云学术

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Deep Learning Approaches for Vehicle and Pedestrian Detection in Adverse Weather

Published:2023-06-21 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Transportation Electrification Conference & Expo (ITEC)
language:
Short-container-title:

Author:

Zaman Mostafa¹,Saha Sujay²,Zohrabi Nasibeh³,Abdelwahed Sherif¹

Affiliation:

1. Virginia Commonwealth University,Department of Electrical and Computer Engineering,Richmond,VA

2. University of Dhaka,Department of Electrical and Electronic Engineering,Dhaka,Bangladesh

3. Pennsylvania State University,Department of Engineering,Media,PA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10186879/10186892/10187020.pdf?arnumber=10187020

Reference39 articles.

1. Vehicle Tracking Using Surveillance With Multimodal Data Fusion

2. Vehicle Target Detection Method Based on Improved SSD Model

3. Combining monocular and stereo-vision for real-time vehicle ranging and tracking on multilane highways

4. Accurate and efficient vehicle detection framework based on SSD algorithm

5. SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches;Algorithms;2024-02-26

2. Enhanced Dynamic Vehicle Detection and Tracking to Improve the Quality of Image Processing Using Deep Learning;Lecture Notes in Networks and Systems;2024

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