Application Deep Learning Vision Based by Shaik Shafiulla (2 results)

Author: 
Title: 
Refine with Advanced Search

Refine your search

  • Books (2)

  • New (2)

to

Custom price range (US$)

to

  • Language: English

    Published by LAP LAMBERT Academic Publishing, 2022

    6204742426 / 9786204742427

    • Softcover

    Seller: preigu, Osnabrück, Germanypreigu

    5-star seller
    Contact seller

    Condition: New

    US$ 60.77

    US$ 78.58 shipping 
    Ships from Germany to U.S.A.

    Quantity: 5 available

    Taschenbuch. Condition: Neu. Application of Deep Learning | Vision-Based Discernment and Counting of Vehicles inLow Illumination Using Deep Learning | Shafiulla Basha Shaik (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786204742427 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

  • Language: English

    Published by LAP LAMBERT Academic Publishing, 2022

    6204742426 / 9786204742427

    • Softcover
    • Print on Demand

    Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

    5-star seller
    Contact seller

    Condition: New

    US$ 79.12

    US$ 39.29 shipping 
    Ships from Germany to U.S.A.

    Quantity: 1 available

    Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The intelligent transportation system has received a lot of attention in recent years. Vehicle detection is an important task in this area where vehicle counting and classification are two important applications. Taking the size of vehicles and illumination factor into consideration the detection remains a challenge. To overcome the above problem we propose aVision-Based Discernment and Counting of Vehicles in Low Illumination Using Deep Learning, In the proposed system, we enhance the low illumination image using the Dual-channel prior-based method. By using the segmentation method the highway road is first extracted and biased into two areas namely remote area and proximal area. Here we use the YOLOv3 network, by placing the above two areas in this network, we can detect the type and position of the vehicle. Finally, by using the ORB algorithm we can judge the direction of the vehicle and obtain a number of different vehicles. The results obtained are such that the accurate count of vehicles especially for small-sized vehicles in low illumination.…