基于 YOLOv7 的轻质路面病害检测模型研究

Chishe Wang, Jun Li, Jie Wang, Weikang Zhao
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摘要

快速的城市化进程使得道路建设和维护势在必行,但道路病害检测耗时且准确性有限。为了克服这些挑战,我们提出了一种高效的 YOLOv7 道路病害检测模型。我们的方法包括集成 MobilieNetV3 作为骨干特征提取网络,以降低网络参数和计算要求。此外,我们还在空间金字塔池化模块中引入了 BRA attention 模块,以消除冗余信息并增强网络的特征表示能力。此外,我们还在骨干网络中使用了 F-ReLU 激活函数,扩大了卷积层的感受野范围。为了优化模型的边界损失,我们采用了 Wise-IoU 损失函数,它更加注重普通样本的质量,提高了网络的整体性能和泛化能力。实验结果表明,改进后的检测算法在南京城区的公共数据集(RDD)和南京数据集(NJdata)上实现了更高的召回率和平均精度(mAP)。具体来说,与 YOLOv7 相比,我们的模型在 RDD 数据集上的召回率和 mAP 分别提高了 3.3% 和 2.6%。在南京数据集上,我们的模型将召回率和 mAP 分别提高了 1.9% 和 1.3%。此外,我们的模型还将参数要求和计算要求分别降低了 30% 和 22.5%,在检测精度和速度之间取得了平衡。总之,我们的道路病害检测模型为解决城市地区道路病害检测所面临的挑战提供了一个有效的解决方案。与现有模型相比,它在准确性、效率和泛化能力方面都有所提高。
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Research on lightweight pavement disease detection model based on YOLOv7
Rapid urbanization has made road construction and maintenance imperative, but detecting road diseases has been time-consuming with limited accuracy. To overcome these challenges, we propose an efficient YOLOv7 road disease detection model. Our approach involves integrating MobilieNetV3 as the backbone feature extraction network to reduce the network’s parameters and computational requirements. Additionally, we introduce the BRA attention module into the spatial pyramid pooling module to eliminate redundant information and enhance the network’s feature representation capability. Moreover, we utilize the F-ReLU activation function in the backbone network, expanding the convolutional layers’ receptive field range. To optimize the model’s boundary loss, we employ the Wise-IoU loss function, which places more emphasis on the quality of ordinary samples and enhances the overall performance and generalization ability of the network. Experimental results demonstrate that our improved detection algorithm achieves a higher recall rate and mean average precision (mAP) on the public dataset (RDD) and the NJdata dataset in Nanjing’s urban area. Specifically, compared to YOLOv7, our model increases the recall rate and mAP on RDD by 3.3% and 2.6%, respectively. On the NJdata dataset, our model improves the recall rate and mAP by 1.9% and 1.3%, respectively. Furthermore, our model reduces parameter and computational requirements by 30% and 22.5%, respectively, striking a balance between detection accuracy and speed. In conclusion, our road disease detection model presents an effective solution to address the challenges associated with road disease detection in urban areas. It offers improved accuracy, efficiency, and generalization capabilities compared to existing models.
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