%0 Generic %1 sultana2019review %A Sultana, F. %A Sufian, A. %A Dutta, P. %D 2019 %K 2019 arxiv cnn detection review %T A Review of Object Detection Models based on Convolutional Neural Network %U http://arxiv.org/abs/1905.01614 %X Convolutional Neural Network (CNN) has become the state-of-the-art for object detection task. In this paper, we have explained different object detection models based on CNN. We have categorized those detection models according to two different approaches: two-stage approach and one-stage approach. Through this paper, we have shown advancements in object detection model from R-CNN to latest RefineDet. We have discussed the model description and training details of each model. We have also drawn a comparison among those models.

@misc{sultana2019review, abstract = {Convolutional Neural Network (CNN) has become the state-of-the-art for object detection task. In this paper, we have explained different object detection models based on CNN. We have categorized those detection models according to two different approaches: two-stage approach and one-stage approach. Through this paper, we have shown advancements in object detection model from R-CNN to latest RefineDet. We have discussed the model description and training details of each model. We have also drawn a comparison among those models.}, added-at = {2019-09-17T21:14:31.000+0200}, author = {Sultana, F. and Sufian, A. and Dutta, P.}, biburl = {https://www.bibsonomy.org/bibtex/222984fac68d74e705f3ba19f00b46735/analyst}, description = {[1905.01614] A Review of Object Detection Models based on Convolutional Neural Network}, interhash = {74799c8dc4c90a3991f7e89cb69fa4f3}, intrahash = {22984fac68d74e705f3ba19f00b46735}, keywords = {2019 arxiv cnn detection review}, note = {cite arxiv:1905.01614Comment: 7 pages, 10 figures, 1 table, Submitted to 2nd International Conference on Communication, Devices and Computing(ICCDC 2019)}, timestamp = {2019-09-17T21:14:31.000+0200}, title = {A Review of Object Detection Models based on Convolutional Neural Network}, url = {http://arxiv.org/abs/1905.01614}, year = 2019 }