Concrete Surface Crack Detection Algorithm Based on Improved YOLOv8.

Concrete surface crack detection is a critical research area for ensuring the safety of infrastructure, such as bridges, tunnels and nuclear power plants, and facilitating timely structural damage repair. Addressing issues in existing methods, such as high cost, lengthy processing times, low efficiency, poor effectiveness and difficulty in application on mobile terminals, this paper proposes an improved lightweight concrete surface crack detection algorithm, YOLOv8-Crack Detection (YOLOv8-CD), based on an improved YOLOv8. The algorithm integrates the strengths of visual attention networks (VANs) and Large Convolutional Attention (LCA) modules, introducing a Large Separable Kernel Attention (LSKA) module for extracting concrete surface crack and local feature information, adapted for features such as fracture susceptibility, large spans and slender shapes, thereby effectively emphasizing crack shapes. The Ghost module in the YOLOv8 backbone efficiently extracts essential information from original features at a minimal cost, enhancing feature extraction capability. Moreover, replacing the original convolution structure with GSConv in the neck network and employing the VoV-GSCSP module adapted for the YOLOv8 framework reduces floating-point operations during feature channel fusion, thereby lowering computational complexity whilst maintaining model accuracy. Experimental results on the RDD2022 and Wall Crack datasets demonstrate the improved algorithm increases in mAP50 by 15.2% and 12.3%, respectively, and in mAP50-95 by 22.7% and 17.2%, respectively, whilst achieving a reduced model computational load of only 7.9 × 109, a decrease of 3.6%. The algorithm achieves a detection speed of 88 FPS, enabling real-time and accurate detection of concrete surface crack targets. Comparison with other mainstream object detection algorithms validates the effectiveness and superiority of the proposed approach.

Pancreas segmentation using a dual-input v-mesh network.

Accurate segmentation of the pancreas from abdomen scans is crucial for the diagnosis and treatment of pancreatic diseases. However, the pancreas is a small, soft and elastic abdominal organ with high anatomical variability and has a low tissue contrast in computed tomography (CT) scans, which makes segmentation tasks challenging. To address this challenge, we propose a dual-input v-mesh fully convolutional network (FCN) to segment the pancreas in abdominal CT images. Specifically, dual inputs, i.e., original CT scans and images processed by a contrast-specific graph-based visual saliency (GBVS) algorithm, are simultaneously sent to the network to improve the contrast of the pancreas and other soft tissues. To further enhance the ability to learn context information and extract distinct features, a v-mesh FCN with an attention mechanism is initially utilized. In addition, we propose a spatial transformation and fusion (SF) module to better capture the geometric information of the pancreas and facilitate feature map fusion. We compare the performance of our method with several baseline and state-of-the-art methods on the publicly available NIH dataset. The comparison results show that our proposed dual-input v-mesh FCN model outperforms previous methods in terms of the Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), average surface distance (ASD) and Hausdorff distance (HD). Moreover, ablation studies show that our proposed modules/structures are critical for effective pancreas segmentation.

The Effect of Fineness on the Hydration Activity Index of Ground Granulated Blast Furnace Slag.

To improve the properties of ground granulated blast furnace slag (GGBS) and utilize ground granulated blast furnace slag efficiently, this study investigates the effect of fineness on the hydration activity index (HAI) of ground granulated blast furnace slag. The hydration activity index of GGBS with six specific surface areas (SSAs) was characterized by the ratio of compressive strength of the prismatic mortar test block. The particle size distribution of GGBS with different grinding times was tested by laser particle size analyzer. The paste of different specific surface area GGBSs in different curing ages was investigated at the micro level by X-ray diffraction, scanning electron microscope, energy dispersive spectrometer, thermogravimetric scanning calorimeter, and differential scanning calorimeter. The effect of particle distribution of GGBS on the hydration activity index of different curing ages was studied by gray correlation analysis. The results indicated that the compressive strength and hydration activity index increases with the increase of a specific surface area of GGBS at different curing ages. The hydration activity index at different curing ages is almost a linear role for specific surface areas. With the increase in the specific surface area of GGBS, the content of Ca(OH)2 in paste decreases gradually. When GGBS was added into a mortar test block, the hydrate calcium silicate gel in paste changed from a high Ca/Si ratio to a low Ca/Si ratio. The 0-10 micron particles of GGBS particle distribution were highly correlated with the hydration activity index at different curing ages.