计算机工程2025，Vol.51Issue(12)：294-303,10.DOI:10.19678/j.issn.1000-3428.0070027
基于YOLOv8的小目标检测模型的优化
Optimization of Small Object Detection Model Based on YOLOv8
摘要
Abstract
Deep learning-based object detection has significantly improved the detection of medium and large targets.However,when detecting small objects,traditional algorithms often face challenges such as missed detections and false positives owing to the inherent issues of small scale and complex backgrounds.Therefore,this study aims to enhance the accuracy of small object detection by improving the YOLOv8 model.First,the convolutional module in the backbone is replaced with the RFAConv module,which enhances the ability of the model to process complex images.Second,a Mixed Local Channel Attention(MLCA)mechanism is introduced in the neck part,allowing the model to fuse features from different layers more efficiently while maintaining computational efficiency.Third,the Detect head of YOLOv8 is replaced with the Detect_FASFF head to address the inconsistency between different feature scales and improve the ability of the model to detect small objects.Finally,the Complete Intersection over Union(CIoU)loss function is replaced with the Focaler-IoU loss function,enabling the model to focus more on small objects that are difficult to locate precisely.Experimental results show that the improved model increases mAP@0.5 by 4.8 percentage points and mAP@0.5:0.95 by 3.0 percentage points on the FloW-Img dataset,which is sparse in small objects.On the VisDrone2019 dataset which has a high density of small objects,mAP@0.5 increases by 5.9 percentage points and mAP@0.5:0.95 improves by 4.0 percentage points.In addition,generalization comparison experiments are conducted on the low-altitude dataset AU-AIR and the pedestrian-dense detection dataset WiderPerson.The optimized model significantly improves the accuracy of small object detection compared with the original model and expands its applicability.关键词
深度学习/YOLOv8网络模型/小目标检测/注意力机制/损失Key words
deep learning/YOLOv8 network model/small object detection/attention mechanism/loss分类
信息技术与安全科学引用本文复制引用
WANG Guoming,JIA Daiwang..基于YOLOv8的小目标检测模型的优化[J].计算机工程,2025,51(12):294-303,10.基金项目
国家自然科学基金青年基金(62102003) （62102003）
安徽省大学生创新创业基金(S202310361157,S202310361161). （S202310361157,S202310361161）
