Aiming at the environment of low illumination, high dust, and heavy water fog in coal mine driving face and the problems of occlusion, coincidence, and irregularity of bolt mesh laid on coal wall, a YOLOv7 bolt mesh-detection algorithm combining the image enhancement and convolutional block attention module is proposed. First, the image brightness is enhanced by a hyperbolic mapping transform-based image enhancement algorithm, and the image is defogged by a dark channel-based image defogging algorithm. Second, by introducing a convolutional block attention model in the YOLOv7 detection network, the significance of bolt mesh targets in the image is improved, and its feature expression ability in the detection network is enhanced. Meanwhile, the original activation function ReLU in the convolutional layer Conv of the YOLOv7 network is replaced by LeakyReLU so that the activation function has stronger nonlinear expression capability, which enhances the feature extraction performance of the network and thus improves the detection accuracy. Finally, the training and testing samples were prepared using the actual video of the drilling and bolting operation, and the proposed algorithm is compared with five classical target detection algorithms. The experimental results show that the proposed algorithm can be better applied to the low illumination, high dust environment, and irregular shape on the detection accuracy of coal mine roadway bolt mesh, and the average detection accuracy of the image can reach 95.4% with an average detection time of 0.0392 s.

<p>In this paper, we propose an adjacent-scale fusion 2.5D U-Net with large-kernel (ASF-LKUNet) for multi-class medical image segmentation tasks. To reduce model complexity, we utilize a u-shaped encoder-decoder as the base architecture of ASF-LKUNet. In the encoder path, we design the large-kernel residual block, which combines the large and small kernels and can simultaneously capture the global and local features while retaining the advantages of ViT. Furthermore, we develop an adjacent-scale GRN channel attention mechanism that incorporates the low-level details with the high-level semantics by fusing the feature of adjacent scales. The adaptive fusion is implemented by the improved large-kernel channel attention based on global response normalization (GRN). In ASF-LKUNet, all the large-kernel apply depth-wise convolutions to further reduce the complexity. Our proposed method is compared with ten other methods, including those based on UNets, multi-scale fusion, 3D CNN, and ViTs. Extensive experiments of performance and interpretability analysis show that ASF-LKUNet outperforms various competing methods with less model complexity on different medical applications, including multi-organ segmentation in CT images and cardiac multi-structure segmentation in MRI images.</p>

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