Name
Abstract | ||
|---|---|---|
The object detection is a challenging problem in computer vision with various potential real-world applications. The objective of this study is to evaluate the deep learning based object detection techniques for detecting drones. In this paper, we have conducted experiments with different Convolutional Neural Network (CNN) based network architectures namely Zeiler and Fergus (ZF), Visual Geometry Group (VGG16) etc. Due to sparse data available for training, networks are trained with pre-trained models using transfer learning. The snapshot of trained models is saved at regular interval during training. The best models having high mean Average Precision (mAP) for each network architecture are used for evaluation on the test dataset. The experimental results show that VGG16 with Faster R-CNN perform better than other architectures on the training dataset. Visual analysis of the test dataset is also presented. |
Year | Venue | Field |
|---|---|---|
2017 | AVSS | Computer science,Convolutional neural network,Transfer of learning,Network architecture,Artificial intelligence,Deep learning,Snapshot (computer storage),Sparse matrix,Computer vision,Object detection,Pattern recognition,Feature extraction,Machine learning |
DocType | Citations | PageRank |
Conference | 5 | 0.50 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Saqib | 1 | 9 | 3.37 |
| Sultan Daud Khan | 2 | 13 | 3.78 |
| Nabin Sharma | 3 | 132 | 11.55 |
| M. Blumenstein | 4 | 168 | 31.87 |