Pavement damage detection model for improved YOLOv8
To overcome big differences in pavement damage scale,difficulties in feature extraction of small damage and the small proportion of defects in images in pavement disease detection,this paper proposes a pavement damage detection method based on improved YOLOv8.First, based on the YOLOv8s network structure, a down-sampling network module without information loss is built by introducing the channel attention mechanism and the no step convolutional network structure, removing the background redundant information and retaining more pavement disease texture features.Second, the ability of the network to capture shallow features is enhanced by building a multi-scale adaptive feature fusion network based on PANet, and the efficient fusion of feature information at different scales is realized.Finally, the Focal Loss function is employed to assign corresponding weights to each sample, which alleviates the imbalance between the positive and negative samples.Our experiments show the proposed method achieves an average precision of 57.1％ and 52.8％ on the RDD2020 and RDD2022 datasets, up by 3.2 and 0.6 percentage points respectively compared with those of the YOLOv8s model,and it performs better than other detection networks such as YOLOv5.
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