Tackling the widespread problems of inaccuracies, slow detection speed, and poor adaptability in small object defect detection on PCB circuits, this study suggests a lightweight printed circuit board surface defect identification algorithm, building upon an improved YOLOv8-PCB. This algorithm first introduces the C2f_SHSA attention mechanism in the backbone network, which unites the merits of channel attention and spatial attention, facilitating an efficient fusion of local and global features in a lightweight manner, thereby enhancing the model’s identification preciseness for small defects. Subsequently, in the neck network, the C2f_IdentityFormer structure, which combines the C2f structure with the IdentityFormer structure, supplants the initial C2f structure. This enhancement improves the model’s sensitivity to subtle features and further optimizes the effect of feature fusion. Eventually, the PIoU is presented to enhance the model’s adaptability to small, complex PCB defects with varying sizes and shapes, while also accelerating the mode’s convergence speed. Experimental outcomes reveal that the improved YOLOv8-PCB algorithm displays remarkable performance in the PCB dataset, with a Recall rate of 94.0%, a mean Average Precision (mAP) of 96.1%, and an F1 score of 94.35%. Moreover, the model’s weight size is only 5.2 MB. Compared to the YOLOv8n baseline model, the Recall rate has a 3.6% improvement, the mAP is raised by 1.8%, and the F1 score is enhanced by 1.9%, while the model’s weight is reduced by 17.46%. The enhancements in performance metrics confirm that the improved algorithm not only fulfills the requirements for efficient and real-time detection in PCB surface defect identification tasks but is also better suited for deployment and operation on edge devices.

Ancient Chinese characters known as oracle bone inscriptions (OBIs) were inscribed on turtle shells and animal bones, and they boast a rich history dating back over 3600 years. The detection of OBIs is one of the most basic tasks in OBI research. The current research aimed to determine the precise location of OBIs with rubbing images. Given the low clarity, severe noise, and cracks in oracle bone inscriptions, the mainstream networks within the realm of deep learning possess low detection accuracy on the OBI detection dataset. To address this issue, this study analyzed the significant research progress in oracle bone script detection both domestically and internationally. Then, based on the YOLOv8 algorithm, according to the characteristics of OBI rubbing images, the algorithm was improved accordingly. The proposed algorithm added a small target detection head, modified the loss function, and embedded a CBAM. The results show that the improved model achieves an F-measure of 84.3%, surpassing the baseline model by approximately 1.8%.

Cropland extraction from remote sensing images is an essential part of precise digital agriculture services. This paper proposed an SSGNet network of multiscale fused extraction of cropland based on the attention mechanism to address issues with complex cropland feature types in remote sensing images that resulted in blurred boundaries and low accuracy in plot partitioning. The proposed network contains different modules, such as spatial gradient guidance and dilated semantic fusion. It employs the image gradient attention guidance module to fully extract cropland plot features. This causes the feature to be transferred from the encoding layer to the decoding layer, creating layers full of key features within the cropland and making the extracted cropland information more accurate. In addition, this study also solves the problem caused by a large amount of spatial feature information, which losses easily during the downsampling process of continuous convolution in the coding layer. Aiming to solve this issue, we put forward a model for consensus fusion of multiscale spatial features to fuse each-layer feature of the coding layer through dilated convolution with different dilated ratios. This approach was proposed to make the segmentation results more comprehensive and complete. The lab findings showed that the Precision, Recall, MIoU, and F1 score of the multiscale fusion segmentation SSGNet network based on the attention mechanism had achieved 93.46%, 90.91%, 85.54%, and 92.73%, respectively. Its segmentation effect on cropland was better than other semantic segmentation networks and can effectively promote cropland semantic extraction.

Optimizing particle grading for dense granular packing remains challenging because the formulation space expands rapidly with increasing particle-size complexity, while direct evaluation using the discrete element method (DEM) is computationally expensive. In this work, a data-driven framework integrating DEM, Gaussian process regression, and Bayesian optimization is developed for efficient screening of ternary particle-grading formulations under a common high-density condition. Normalized residual interparticle overlap is introduced as a fixed-density measure of geometric incompatibility, which enables different formulations to be compared without repeatedly determining their maximum packing states. The surrogate-assisted search increases the occurrence of low-overlap candidates and shows that favorable formulations are distributed among several regions of the grading space rather than converging to a unique optimum. Repeat evaluations further identify two low-overlap strategies with distinct particle-size distributions and stable packing responses. Contact network and particle rearrangement analyses show that low overlap can be achieved through either a coarse–intermediate framework assisted by local fine-particle accommodation or a more distributed multiscale contact network. Fine particles provide most of the configurational mobility during relaxation, while the distribution of this mobility depends strongly on the underlying grading architecture. These findings establish practical relationships among particle-size hierarchy, distribution breadth, component fraction, and packing response, which can guide the selection of dense powder formulations and reduce reliance on extensive trial-and-error screening.