基于改进YOLOv7的桥梁裂缝检测算法

Bridge Crack Detection Algorithm Based on Improved YOLOv7

摘要: 针对当前桥梁裂缝检测算法存在的错检、漏检等问题，本文对现有桥梁裂缝检测算法的进行了改进。首先，引入CBAM注意力机制，增强网络对裂缝边缘特征的提取能力，提升模型的检测精度；其次，基于SPPF对SPP的改进方法，使用改进后的SPPFCSPC模块替换SPPCSPC模块；最后，采用WIOU损失函数，提升了网络模型的收敛速度。经实验验证，本文改进的模型对桥梁裂缝的检测精度高达87.1%，较YOLOv7模型提高了8.8%，mAP值为85.4%，较YOLOv7模型提高了9.3%，能够满足当前桥梁裂缝检测需求。

Abstract: Aiming at the current bridge crack detection algorithm’s problems such as misdetection and omission, this paper improves the existing bridge crack detection algorithm. Firstly, the CBAM attention mechanism is introduced to enhance the network’s ability to extract features from the crack edges and improve the detection accuracy of the model; secondly, based on the improvement method of SPPF to SPP, the SPPCSPC module is replaced by the improved SPPFCSPC module; finally, the WIOU loss function is adopted to improve the convergence speed of the network model. After experimental verification, the improved model in this paper has a high detection accuracy of 87.1%, for bridge cracks which is 8.8% higher than the YOLOv7 model, and the mAP value is 85.4%, which is 9.3% higher than the YOLOv7 model, and it can satisfy the current demand for bridge crack detection.