MFPNet: using multi-type features parallelism in deep layers to improve segmentation performance for pavement cracks

Abstract

Aiming at the difficulty of accurately segmenting pavement cracks in traditional detection methods, this paper proposes a lightweight real-time detection model named MFPNet with an end-to-end encoding and decoding structure. Firstly, in the encoding stage, based on the different extraction characteristics of the involution-G and convolution operators for cracks, the designed multi-type features parallel (MFP) module is used in the deep network to enhance the abstract semantic information with reducing information loss. Then, the simplified long connection structure is adopted in the decoding stage to maintain the detection speed without reducing the detection accuracy. Additionally, ablation experiments demonstrate the effectiveness of the designed module. What’s more, compared with other deep learning-based algorithms, the model proposed in this paper has excellent performance, and its MIOU, Recall, and F1 Score reach 0.7705, 0.8023, and 0.8485, respectively. In practice, MFPNet can be implemented in images with a high resolution of 2048×1024 in real time.