Tianyong Jiang, Lingyun Li, Bijan Samali, Yang Yu, Ke Huang, Wanli Yan, Lei Wang
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引用次数: 0
摘要
为了解决传统混凝土桥梁多损伤识别方法固有的识别精度低、速度慢、泛化能力弱等难题,本文提出了一种高效的轻量级损伤识别模型,该模型是在 You only look once v4(YOLOv4)的基础上,利用 MobileNetv3 和融合反转残差块构建而成,命名为 YOLOMF。首先,构建了一个名为 MobileNetv3 和融合反转残差(MobileNetv3-FusedIR)的新型轻量级网络,作为 YOLOMF 的主干网络。这是通过将融合移动倒置瓶颈卷积(Fused-MPConv)集成到 MobileNetv3 的浅层来实现的。其次,YOLOv4 中的标准卷积被深度可分离卷积所取代,从而减少了模型的参数数量和复杂度。第三,深入研究了不同激活函数对 YOLOMF 损伤识别性能的影响。最后,为了验证所提出的方法在复杂环境中的有效性,我们使用了一个名为 Imgaug 的数据增强库来模拟混凝土桥梁在运动模糊、雾、雨、雪、噪声和颜色变化等挑战条件下的损坏图像。结果表明,YOLOMF 在不同视场尺寸和复杂环境条件下对混凝土桥梁的多损伤识别能力非常出色。YOLOMF 的检测速度达到 85f/s,有助于在复杂环境下对混凝土桥梁进行有效的多损伤实时检测。
Lightweight object detection network for multi-damage recognition of concrete bridges in complex environments
To solve the challenges of low recognition accuracy, slow speed, and weak generalization ability inherent in traditional methods for multi-damage recognition of concrete bridges, this paper proposed an efficient lightweight damage recognition model, constructed by improving the you only look once v4 (YOLOv4) with MobileNetv3 and fused inverted residual blocks, named YOLOMF. First, a novel lightweight network named MobileNetv3 with fused inverted residual (MobileNetv3-FusedIR) is constructed as the backbone network for YOLOMF. This is achieved by integrating the fused mobile inverted bottleneck convolution (Fused-MBConv) into the shallow layers of MobileNetv3. Second, the standard convolution in YOLOv4 is replaced with the depthwise separable convolution, resulting in a reduction in the number of parameters and complexity of the model. Third, the effects of different activation functions on the damage recognition performance of YOLOMF are thoroughly investigated. Finally, to verify the effectiveness of the proposed method in complex environments, a data enhancement library named Imgaug is used to simulate concrete bridge damage images under challenging conditions such as motion blur, fog, rain, snow, noise, and color variations. The results indicate that the YOLOMF shows excellent multi-damage recognition proficiency for concrete bridges across varying field-of-view sizes as well as complex environmental conditions. The detection speed of YOLOMF reaches 85f/s, facilitating effective real-time multi-damage detection for concrete bridges under complex environments.
期刊介绍:
Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms.
Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.