| Title |
Light-weight Deep Neural Network for Small Vehicle Detection using Model-scale YOLOv4 |
| Authors |
(Mingi Kim) ; (Heegwang Kim) ; (Chanyeong Park) ; (Joonki Paik) |
| DOI |
https://doi.org/10.5573/IEIESPC.2023.12.5.369 |
| Keywords |
Small object detection; Deep learning; Light-weight; Attention mechanism |
| Abstract |
In this paper, we present a light-weight deep neural network based on an efficiently scaled YOLOv4 model for detecting small objects in drone images. Since drone-captured images mainly contain small objects, we modified the YOLOv4 model by eliminating the head layer responsible for detecting large objects. This modification significantly reduced the model's parameters and processing time for non-maximum suppression (NMS). Moreover, the appropriately scaled model for small object detection can be used on a drone. To achieve a light-weight network for small object detection with minimal performance degradation, we used the attention stacked hourglass network (ASHN) for feature fusion. In extensive experiments, the proposed network outperformed the baseline network in several datasets. |