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.

中文翻译：

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用于复杂环境下混凝土桥梁多损伤识别的轻量级目标检测网络

针对传统混凝土桥梁多损伤识别方法存在的识别精度低、速度慢、泛化能力弱的挑战，本文通过改进you only Look Once v4（YOLOv4），提出了一种高效的轻量级损伤识别模型。 ）与 MobileNetv3 和融合反向残差块，命名为 YOLOMF。首先，构建了一种名为 MobileNetv3 的新型轻量级网络，具有融合逆残差（MobileNetv3-FusedIR）作为 YOLOMF 的骨干网络。这是通过将融合移动反向瓶颈卷积（Fused-MBConv）集成到 MobileNetv3 的浅层中来实现的。其次，YOLOv4中的标准卷积被替换为深度可分离卷积，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，为了验证该方法在复杂环境中的有效性，使用名为 Imgaug 的数据增强库来模拟运动模糊、雾、雨、雪、噪声和颜色变化等挑战性条件下的混凝土桥梁损伤图像。结果表明，YOLOMF 对不同视场大小和复杂环境条件下的混凝土桥梁表现出出色的多重损伤识别能力。 YOLOMF的检测速度达到85f/s，有助于对复杂环境下的混凝土桥梁进行有效的实时多损伤检测。