Name
Abstract | ||
|---|---|---|
The research problems on Object detection have been attracted with major issues in the computer vision domain. Object detection based on images from unmanned aerial vehicles (UAV) - drones, has versatile applications in both defence security, agriculture and GIS. However, real-time object detection in UAV scenarios remains quite a tedious problem due to environmental obstructions such as occlusion and view-invariant conditions despite the high number of solutions proposed to solve this task. This paper proposes an improved YOLOv3-tiny object detector by introducing a multi-dilated module between the convolution unit and the receptive field, where the problem of a small number of positive training samples is solved by a larger size of the predicted feature map thereby reducing the rate of label rewriting in YOLOv3-tiny. We find that the fusion of multi-scale receptive fields is effective in detecting even every single tiny object. We introduce a path aggregation module that merges the semantic information in a deeper layer and detailed information in an earlier layer. The analysis of the proposed solution shows that on the VisDrone2019-Det test set, our proposed model is more efficient and effective, running 2.96% times faster and increasing 4.0% AP50 than YOLOv3. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/IDSTA55301.2022.9923041 | 2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) |
Keywords | DocType | ISBN |
Convolutional Neural Network,Dilated CNN,Feature Fusion,Object Detection,Real-Time Object Detection,Unmanned Aerial Vehicle,YOLOv3 | Conference | 978-1-6654-9961-3 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naresh Kumar | 1 | 0 | 0.34 |
| Abdul Khadar Jilani | 2 | 0 | 0.34 |
| Pavan Kumar | 3 | 0 | 0.34 |
| Anastasija Nikiforova | 4 | 0 | 0.68 |