Research on Optimization of Pavement Distress Identification Model in Complex Environmen
Automatic detection of pavement distress is a hot research direction in the field of road engineering.Because of the particularity of pavement distress and the complexity of background,there are some problems in automatic detection,such as low accuracy and poor generalization.Aiming at this phenomenon,this paper adopts YOLOv5 as the baseline model,adds efficient feature extraction modules C2F and CBAM to the backbone network and neck network,and adds micro detectors to the detector head network,forming an optimized network structure.A large number of road images with complex background were collected by car-mounted high-definition camera,and 67 942 road images were marked for model training,and the self-built road distress data set was enhanced by Mosaic algorithm and MixUP algorithm.The hyperparameter is optimized and the loss function is optimized during training.Finally,the influence of optimization measures of model network on the accuracy of pavement distress detection model is explored by setting ablation experiments and comparative experiments.The research results show that the application of C2F and CBAM modules can effectively improve the feature extraction ability of the network,and the detection ability of the model can be enhanced at multiple scales by increasing the micro-detector.The accuracy and recall of the network model optimized by the above three measures have increased by 11.88％and 8.69％,and the mAP has achieved a high score of 0.719.From the point of view of distress types,this model improves the detection accuracy of longitudinal cracks,transverse cracks and pits,especially pits.It shows that the model in this paper has excellent detection ability in identifying small-scale objects such as pavement distress.
road and railway engineeringpavement structurenimage processingcrack detection
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