Using an improved U-Net++ with a T-Max-Avg-Pooling layer as a rapid approach for concrete crack detection

Abstract

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The monitoring of concrete structures has advanced remarkably with the aid of deep learning technologies. Since concrete is multi-purpose and low-cost, it is extensively used for construction purposes. Concrete is very enduring. Nevertheless, it tends to crack which endangers the integrity of the structure and results in complications. The current study offers a new image segmentation approach for detecting cracks in concrete by making use of an optimized U-Net++ architecture. The proposed model gives the features of the T-Max-Avg Pooling layer which effectively combines the advantages of traditional max and average pooling using a learnable parameter to balance feature extraction dynamically. This innovation both improves the output accuracy and processing speed and captures the fine details. In addition, it mitigates noise and transcends the limitations of conventional pooling methods. Moreover, using learnable pruning and shortening skip connections in U-Net++ reduce redundant computations, making the model faster without compromising accuracy. In comparison with other models like Mask R-CNN and VGG-U-Net, the proposed model had considerably faster inference times (21.01 ms per image) and fewer computational requirements (40G FLOPs), making it very suitable for real-time monitoring applications. The DeepCrack and Concrete Pavement Crack datasets were employed to assess the model thoroughly which yielded an MIoU score of 82.1%, an F1 score of 90.12%, a Dice loss score of 93.7%, and an overall accuracy of 97.65%. According to the results, the enhanced U-Net++ with T-Max-Avg Pooling provided a balanced trade-off between segmentation accuracy and computational efficiency. This indicates its considerable potential for automated real-time crack detection in concrete structures by employing resource-constrained environments including drones and mobile platforms.

Keywords

• concrete crack detection
• deep learning
• image segmentation
• skip connection pruning
• U-Net++ architecture