Research on multi-scale feature detection of open-pit mine road cracks

Abstract

Road crack detection in open-pit mines is of great significance for ensuring the safety and efficiency of mining production. Traditional detection methods and existing deep learning-based approaches have numerous limitations. This paper proposes an open-pit mine road crack detection method based on feature fusion, which introduces an Adaptive Feature Fusion Module (AF2M) and a Channel-Spatial Attention Module (CASP) into the original U-Net network, and optimizes the model using the Layer-Adaptive Magnitude Pruning (LAMP) algorithm.The AF2M module first unifies the scale of four-level multi-scale feature maps from the decoder through upsampling, concatenates them along the channel dimension, then exploits channel dependencies via the ECA (Efficient Channel Attention) module and fuses global-local contextual information using the GC (Global Context) module. It dynamically weights features from different dimensions to enhance key crack features and reduce background interference, whose multi-scale fusion capability surpasses the inherent processing capacity of the U-Net’s native encoder-decoder structure.The CASP module innovatively applies the multi-head self-attention mechanism to channel-wise interaction: it reconstructs channel attention through dimension transformation and QTK (Query-Key-Value) matrix operations, then fuses pooled spatial information to impose spatial attention. Compared with traditional attention mechanisms such as SE (Squeeze-and-Excitation) and CBAM (Convolutional Block Attention Module), it achieves deep synergy of channel-spatial information and improves crack localization accuracy.The LAMP algorithm rescales and sorts the weights of each layer through a unique scoring mechanism, adaptively assigns sparsity at the layer level, and prunes redundant weights within a pruning rate range of 0-0.3 (non-uniformly applied to all layers), ensuring that key feature extraction remains unaffected.Experiments were conducted on a dataset consisting of 2,847 high-resolution images collected from an open-pit coal mine in Inner Mongolia. The results show that the improved model achieves a mean Intersection over Union (mIoU) of 0.83, precision of 0.89, and F1-score of 0.82, representing improvements of 7%, 7%, and 9% respectively compared to the original U-Net. Additionally, the model parameters are reduced to 4.73 M (a 24.1% decrease), the Floating-Point Operations (FLOPs) are 4.25G (a 28.7% decrease), and the inference time per image is 0.30s (a 33.3% speedup).This method exhibits significant advantages in detection accuracy and model complexity, and can effectively meet the requirements of open-pit mine road crack detection, providing a reliable basis for mine road maintenance. Combined with technologies such as UAVs (Unmanned Aerial Vehicles) and GIS (Geographic Information Systems), it is expected to promote the intelligent development of open-pit mine road maintenance.