Light-weight Deep Neural Network for Small Vehicle Detection using Model-scale YOLOv4

被引：0

作者：

Kim M. ^{[1
]}

Kim H. ^{[2
]}

Park C. ^{[2
]}

Paik J. ^{[1
,2
]}

机构：

[1] Graduate School of Artificial Intelligence, Chung-Ang University, Seoul

[2] Department of Image, Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul

来源：

IEIE Transactions on Smart Processing and Computing | 2023年 / 12卷 / 05期

关键词：

Attention mechanism; Deep learning; Light-weight; Small object detection;

D O I：

10.5573/IEIESPC.2023.12.5.369

中图分类号：

学科分类号：

摘要：

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. © 2023 Institute of Electronics and Information Engineers. All rights reserved.

引用

页码：369 / 378

页数：9

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