SPDD-YOLO for Small Object Detection in UAV Images
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Aerial images captured by drones often suffer from blurriness and low resolution, which is particularly problematic for small targets. In such scenarios, the YOLO object detection algorithm tends to confuse or misidentify targets like bicycles and tricycles due to the complex features and local similarities. To address these issues, this paper proposes a SPDD-YOLO model based on YOLOv8. Firstly, the model enhances its ability to extract local features of small targets by introducing the Spatial-to- Depth Module (SPDM). Secondly, addressing the issue that SPDM reduces the receptive field, leading the model to overly focus on local features, we introduced Deep Separable Dilated Convolution (DSDC), which expands the receptive field while reducing parameters and forms the Deep Dilated Module (DDM) together with SPDM. Experiments on the VisDrone2019 dataset demonstrate that the proposed model improved precision, recall, and mAP50 by 5.8%, 5.7%, and 6.4%, respectively.
Description
CCS Concepts: Computing methodologies → Object recognition; Object identification
@inproceedings{10.2312:pg.20241327,
booktitle = {Pacific Graphics Conference Papers and Posters},
editor = {Chen, Renjie and Ritschel, Tobias and Whiting, Emily},
title = {{SPDD-YOLO for Small Object Detection in UAV Images}},
author = {Xue, Xiang and Ji, Ya Tu and Liu, Yang and Xu, H. T. and Ren, Q. D. E. J. and Shi, B. and Wu, N. E. and Lu, M. and Zhuang, X. F.},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-250-9},
DOI = {10.2312/pg.20241327}
}