A Method of Potentially Promising Network for Crack Detection With Enhanced Convolution and Dynamic Feature Fusion

Though crack detection is an indispensable task to ensure the safety of various infrastructures, it is often hard to fully and accurately detect cracks due to their complex background noises as well as long and sharp topological features. To solve this problem, we proposed a network that combines Enhanced Convolution and Dynamic Feature Fusion (ECDFFNet) to improve its overall performance in both capturing long-range dependencies and focus on the local details. In this proposed network, the conventional convolution is replaced by an enhanced one, and a strip mixed convolutional module is embedded in the last two stages of its convolution layers, forming the Enhanced Convolution. Multi-scale information can greatly benefit the crack detection task if they are well fused. Current methods assign a fixed weight to the different scale features regardless of the differences between the local details and semantics. We proposed a Dynamic Feature Fusion (DFF) strategy to the adaptive fusion of different scale features. Extensive experiments on color crack image datasets, i.e., Crack500, CFD, and DeepCrack, show that the proposed model achieved ODS (Optimal Dataset Scale) values of 0.788, 0.863 and 0.872, respectively, and maintained a fast speed of 6 FPS on average in the DeepCrack Dataset. Compared with SegNet, HED, RCF, U-Net, U-HDN, DeepCrack, FPHBN, and DeepCrackT, the proposed method made improvements by 11.5, 8.3, 7, 4.9, 0.5, 4.7, 5.6, and 2.6 respectively, in ODS values.