EFFDet: A Crack Detector via Boundary Preservation and Cross-Attention Integration

The complexity of scenes and the topology of cracks make road crack detection a challenging task. Compared to other semantic segmentation tasks, this mission places a greater demand on the network's ability to preserve detailed boundary information. To address this, a novel road crack detection network architecture EFFDet is proposed in this paper. Firstly, we redesign the encoding-decoding module based on large-scale convolutional kernels and attention mechanisms to reduce the loss of detailed information caused by downsampling. Secondly, the Cross Attention module is proposed to integrate more precise details into the output of the decoding layer. In comparative experiments on four datasets, CRACK500, Volker, CrackLS315 and DeepCrack, EFFDet achieves ODS values of 0.7434, 0.6758, 0.6449 and 0.8708, respectively. The experimental results show that EFFDet demonstrates stronger detection capabilities in road crack detection.