EAFNet: Extraction-amplification-fusion network for tiny cracks detection
Abstract
Tiny cracks are often overlooked in the inspection process, causing huge economic losses and dangerous accidents. Therefore, tiny cracks should be detected in a timely and accurate manner to eliminate the disease at the initial stage. Inspired by the fact that humans are more likely to capture conspicuous information when observing objects, we propose a novel three-stage Extraction-Amplification-Fusion network (EAFNet). Specifically, in the extraction stage, we utilize an effective backbone network for feature extraction of tiny cracks. In the amplification stage, we design a Tiny Feature Amplification (TFA) module to amplify the extracted features. In the fusion stage, we propose a Two-Branch Fusion (TBF) module to fully fuse the feature maps at different resolutions. To make tiny crack information more 'conspicuous', we propose an activation function TinyReLU to enhance the contrast of the tiny cracks with the background. In addition, we construct a Tiny Crack (T-CRACK) dataset with six different backgrounds and a Cross-scale Crack (C-CRACK) dataset. On both datasets, EAFNet achieves an advantage over the existing 8 advanced networks. The two datasets are available at: https://github. com/EAFNet/EAFNet.
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