• Arunkumar S
• Anuroop Sai Reddy
• Shilpa J
• Muthurasu N

Vehicle identification is particularly testing when vehicles are impeded which is regular in heterogeneous rush hour gridlock. As of late Deep Learning has indicated surprising guarantee in understanding numerous PC vision undertakings, for example, object acknowledgment, recognition, and following. In any case, preparing profound learning designs require gigantic marked datasets which are tedious and costly to obtain. We bypass this issue by information growth. By appropriately expanding a current vast small-scale traffic dataset with a little low goal of heterogeneous information, we acquire best in class vehicle location execution.

"Deep Learning Model For Vehicle Detection Using Heterogeneous Information", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.580-583, April-2019, Available :http://www.jetir.org/papers/JETIR1904797.pdf

"Deep Learning Model For Vehicle Detection Using Heterogeneous Information", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp580-583, April-2019, Available at : http://www.jetir.org/papers/JETIR1904797.pdf