Unmanned Aerial Vehicle Image Target Detection Algorithm Based on YOLOv8

doi:10.19678/j.issn.1000-3428.0068268

Abstract

Abstract:

In the Unmanned Aerial Vehicle(UAV) target detection task, missed and false detections are caused by the small size of the detection target and complex background of the detection image. To address the problem of small target detection, the UAV image target detection algorithm is proposed by improving YOLOv8s. First, for application scenarios where drone shooting targets are generally small, the number of Backbone layers of the algorithm is reduced, and the size of the feature map to be detected is increased such that the network model can focus more on small targets. Second, because a certain number of low-quality examples commonly influence the training effect in the dataset, the Wise-IoU loss function is introduced to enhance the training effect of the dataset. Third, by introducing a context enhancement module, the characteristic information of small targets in different receptive fields is obtained, and the positioning and classification effect of the network model on small targets in complex environments is improved. Finally, a spatial-channel filtering module is designed to enhance the characteristic information of the target during the convolution process to filter out useless interference information and address the problem of some small target characteristic information being submerged and lost during the convolution process. Experiment results on the VisDrone2019 dataset demonstrate that the average detection accuracy(mAP@0.5) of the proposed algorithm reaches 45.4%, which is 7.3 percentage points higher than that of the original YOLOv8s algorithm, and the number of parameters is reduced by 26.13%. Under similar experimental conditions, compared with other common small target detection algorithms, the detection accuracy and speed are improved to a certain extent.

Key words: target detection, Unmanned Aerial Vehicle(UAV), small target, filtering, improved YOLOv8 algorithm, attention mechanism

摘要：

在无人机(UAV)目标检测任务中, 存在因检测目标尺度小、检测图像背景复杂等原因导致的漏检、误检问题。针对上述问题, 提出改进YOLOv8s的无人机图像目标检测算法。首先, 针对无人机拍摄目标普遍为小目标的应用场景, 减少算法骨干网络(Backbone)层数, 增大待检测特征图尺寸, 使得网络模型更专注于微小目标; 其次, 针对数据集普遍存在一定数量低质量示例影响训练效果的问题, 引入Wise-IoU损失函数, 增强数据集训练效果; 再次, 通过引入上下文增强模块, 获得小目标在不同感受野下的特征信息, 改善算法在复杂环境下对小目标的定位和分类效果; 最后, 设计空间-通道滤波模块, 增强卷积过程中目标的特征信息, 滤除无用的干扰信息, 改善卷积过程中部分微小目标特征信息被淹没、丢失的现象。在VisDrone2019数据集上的实验结果表明, 该算法的平均检测精度(mAP@0.5)达到45.4%, 相较于原始YOLOv8s算法提高7.3个百分点, 参数量减少26.13%。在相同实验条件下, 相比其他常见小目标检测算法, 检测精度和检测速度也有一定提升。

关键词: 目标检测, 无人机, 小目标, 滤波, 改进YOLOv8算法, 注意力机制

Jida ZHAO, Guoyong ZHEN, Chengqun CHU. Unmanned Aerial Vehicle Image Target Detection Algorithm Based on YOLOv8[J]. Computer Engineering, 2024, 50(4): 113-120.

赵继达, 甄国涌, 储成群. 基于YOLOv8的无人机图像目标检测算法[J]. 计算机工程, 2024, 50(4): 113-120.

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Figures/Tables 12

Fig.1 Structure of C2f module

Fig.2 Structure of decoupling head model

Fig.3 Structure of the proposed target detection algorithm model

Fig.4 Structure of CAM

Fig.5 Structure of SCFM

Fig.6 Structure of CPM

Fig.7 Comparison of target detection accuracy under different loss functions

Fig.8 Visual comparison of target detection effects in ablation experiment

Fig.9 Visual comparison of target detection effects in comparative experiment

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