Light pavement disease detection method based on optimized YOLOX
Building a large convolutional neural network to improve the model accuracy is an effective method.However,limited by small embedded devices and mobile devices limited computing resources,large network model is difficult to deploy in such application scenarios.To solve this problem,this paper presents an efficient pavement disease recognition model based on YOLOX,which has high detection accuracy even in the application scenarios with limited computational resources.First,the YOLOX backbone network is replaced with the opti-mized GhostNet to reduce the network computing parameters,and by referring to the advantages of convolutional block attention module and adaptive adjustment information in space and channel direction,the DAM(Dimen-sional Attention Model)is built to replace the SE module in the GhostBottleneck module,so as to make full use of the limited network capacity for reinforcement feature learning.Secondly,the DFM(Deep Fusion Model)module is proposed to improve the PANet and to deeply integrate the high and low feature layers to obtain richer feature information to improve the detection ability.Thirdly,the Complete-IoU Loss is introduced to fit a more accurate detection box position,reduce direction misjudgment and improve the detection efficiency.Finally,the Image-Multitasking data enhancement method is used to enhance the target image tasking,improving the general-ization ability and robustness of the network.Model comparison was performed on the RDD2020 dataset,as shown by experiments,the improved GhostNet-YOLOX network achieved an mAP of 84.05%,higher than the existing YOLOX-s(66.26%),the number of model parameters is reduced to 14.53 MB,less than YOLOX-s(34.21 MB).Meanwhile,the number of frames of the actual detected video reached 26 p·s-1,Raised by 5.88 p·s-1,the real-time detection is significantly improved.
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