Research on road Damage Detection Based on Improved YOLOv8
DOI:
https://doi.org/10.54097/vnjm0m76Keywords:
Road detection; Deep learning;YOLOv8.Abstract
Aiming at the low accuracy of deep learning in road damage detection, a road damage detection method based on improved YOLOv8 is proposed. Firstly, SENetv2 attention module was added to the backbone network of YOLOv8 to improve the model's feature learning ability. Secondly, in the neck network, GSConv is used to replace the common convolutional module to reduce the complexity of the model and improve the accuracy. Finally, the loss function is changed to Wise-iou to reduce the impact of detection due to a small number of low-quality instances. The experimental results show that compared with the traditional YOLOv8, the mAP50 of this model is increased by 2.1 percentage points, and the detection effect is good, which can meet the requirements of accurate detection.
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