Damaged Car Detection Using Multiple Convolutional Neural Network

Registration ID: IJNRD_194934

Published ID: IJNRD2305334

DOI: Click Here to Get

Gopikrishnan M , Padmanabapushkaran K , Vasanth S , Rakesh R

Machine Learning, Data Science, car damage, CNN

Computer vision and machine learning are both used in the investigation of visual image classification. Assigning an object to a category, or group of categories, that it belongs to, is the work of visually categorizing an object. A two-layered system is typically used to conduct visual classification tasks. It consists of a first layer using an off-the-shelf feature extractor and detector and a second classifier layer. Convolutional neural networks have been demonstrated to surpass such hitherto employed algorithms in recent years. The ability to automatically categorize automotive damage is very desirable, especially for the auto insurance sector, given the importance of cars in today's society. Automobile inspections are a common occurrence for auto insurance providers. Such inspections are labor-intensive, manual, and occasionally flawed processes. processes that expense and annoy customers and insurance firms equally. Even while complete automation of such manual inspection procedures may still be some time off, modern technology may make it feasible to create systems that facilitate, expedite, or improve the process.

Published Paper   E-Certificate

Gopikrishnan M, Padmanabapushkaran K, Vasanth S, & Rakesh R (May-2023). Damaged Car Detection Using Multiple Convolutional Neural Network. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), d241-d246. https://ijnrd.org/papers/IJNRD2305334.pdf

Volume 8 Issue 5, May-2023

Pages : d241-d246

Paper Reg. ID: IJNRD_194934

Published Paper Id: IJNRD2305334

Downloads: 000121974

Research Area: Computer Science & Technology 

Country: Chennai, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305334.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305334

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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