This paper proposes an integrated framework for automatically segmenting road surface cracks that utilize a Multi-Attention-Network and a modified U-Net, combined through neural network stacking, to segment the crack regions accurately. To evaluate the effectiveness of the proposed framework, we introduce a road crack dataset containing complex environmental noise. We explore several stacking scenarios and perform thorough evaluations to assess the performance of the proposed model. Our results show that the proposed method improves the IOU score of 1.5% compared to the original network, indicating its effectiveness in segmenting road cracks. The proposed framework can be a valuable tool for road maintenance and inspection, enabling timely detection and repair of cracks and improving road safety and longevity. Our findings demonstrate the importance of exploring various stacking scenarios and performing comprehensive evaluations to establish the efficacy of the proposed framework.
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