Research on Crack Surface Recognition Method Based on Deep Learning

Wang, Kaiwen; Zhang, Mingfei; Qi, Ling; Dou, Guotao; Zhao, Huapeng

doi:10.2139/ssrn.4370102

Cited by 1 publication

(1 citation statement)

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“…Cai Fenghuang et al [8] proposed improving the YOLOv3 convolutional neural network model by adding an attention mechanism to identify cracks, achieving good detection results. Ying Junjie et al [9] proposed adding a residual module to the UNet network, achieving excellent detection results. Zhou Qingsong et al [10] proposed adding a fixed block size pooling module to improve YOLOv3, accurately identifying bridge disease problems.…”

Section: Introductionmentioning

confidence: 99%

Research on bridge surface defects based on YOLOv7

jiang,

zhang,

zhang

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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This paper explores deep learning methods for bridge surface defect detection, particularly an improved model based on YOLOv7. To address the detection of bridge-type defects, a Dual-Stream Attention Module (DSAM) and a Hybrid Atrous Pyramid Module (DP) were introduced, aiming to enhance the model's capability to capture key features and the efficiency of multi-scale feature extraction. Experimental results show that the improved model demonstrates higher detection accuracy on a bridge defect dataset, with the YOLOv7+DSAM+DP model achieving a mean Average Precision (mAP) of 91.3%. Additionally, the study compares the SSD and Faster R-CNN networks, confirming the superiority of the proposed model. Overall, the model presented in this paper significantly improves the precision and efficiency of bridge surface defect detection by integrating attention mechanisms and hybrid atrous pyramid modules.

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Section: Introductionmentioning

confidence: 99%