电气技术2024，Vol.25Issue(12)：12-20,27,10.
基于改进YOLOv8的街景图像变压器目标检测
Transformer object detection in street view images based on improved YOLOv8
廖方舟 1杨晓霞 1杨容浩 2施琪琦2
作者信息
- 1. 成都理工大学地理与规划学院,成都 610059
- 2. 成都理工大学地球与行星科学学院,成都 610059
- 折叠
摘要
Abstract
Street view images are a form of geospatial big data at the urban street level.Utilizing street view images not only enables large-scale and efficient transformer inspection but also reduces inspection costs.However,transformers in street view images often have few pixels,low resolution and complex backgrounds,leading to unsatisfactory precision of existing object detection methods.To address these issues,this paper proposes an improved YOLOv8 algorithm named YOLOv8-WSX.Firstly,wise intersection over union(WIoU)is used as the loss function to strengthen the detection performance of the algorithm for difficult samples.Secondly,the spatial group-wise enhance(SGE)attention mechanism module is introduced to improve the feature extraction ability of the algorithm.Finally,an extra-small object detection head is added to solve the problem of missing detection of extra-small transformer objects.The experimental results show that compared to YOLOv8,YOLOv8-WSX increases the F1 score by 5.9 percentage points,increases the mean average precision by 6.3 percentage points for IoU at 50%,and increases the mean average precision by 3.2 percentage points for IoU from 50%to 95%.Additionally,the model has fewer parameters.关键词
变压器目标检测/YOLOv8/深度学习/街景图像Key words
transformer object detection/YOLOv8/deep learning/street view images引用本文复制引用
廖方舟,杨晓霞,杨容浩,施琪琦..基于改进YOLOv8的街景图像变压器目标检测[J].电气技术,2024,25(12):12-20,27,10.
