基于改进YOLOv8的街景图像变压器目标检测

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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.

Key words： transformer object detection YOLOv8 deep learning street view images

Received: 14 June 2024

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	Articles by authors
	LIAO Fangzhou
	YANG Xiaoxia
	YANG Ronghao
	SHI Qiqi

Cite this article:

LIAO Fangzhou,YANG Xiaoxia,YANG Ronghao等. Transformer object detection in street view images based on improved YOLOv8[J]. Electrical Engineering, 2024, 25(12): 12-20.

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https://dqjs.cesmedia.cn/EN/Y2024/V25/I12/12

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