Research on improved gangue target detection algorithm based on Yolov8s.

An improved algorithm based on Yolov8s is proposed to address the slower speed, higher number of parameters, and larger computational cost of deep learning in coal gangue target detection. A lightweight network, Fasternet, is used as the backbone to increase the speed of object detection and reduce the model complexity. By replacing Slimneck with the C2F part in the HEAD module, the aim is to reduce model complexity and improve detection accuracy. The detection accuracy is effectively improved by replacing the Detect layer with Detect-DyHead. The introduction of DIoU loss function instead of CIoU loss function and the combination of BAM block attention mechanism makes the model pay more attention to critical features, which further improves the detection performance. The results show that the improved model compresses the storage size of the model by 28%, reduces the number of parameters by 28.8%, reduces the computational effort by 34.8%, and improves the detection accuracy by 2.5% compared to the original model. The Yolov8s-change model provides a fast, real-time and efficient detection solution for gangue sorting. This provides a strong support for the intelligent sorting of coal gangue.

Yolov8n-FADS: A Study for Enhancing Miners' Helmet Detection Accuracy in Complex Underground Environments.

A new algorithm, Yolov8n-FADS, has been proposed with the aim of improving the accuracy of miners' helmet detection algorithms in complex underground environments. By replacing the head part with Attentional Sequence Fusion (ASF) and introducing the P2 detection layer, the ASF-P2 structure is able to comprehensively extract the global and local feature information of the image, and the improvement in the backbone part is able to capture the spatially sparsely distributed features more efficiently, which improves the model's ability to perceive complex patterns. The improved detection head, SEAMHead by the SEAM module, can handle occlusion more effectively. The Focal Loss module can improve the model's ability to detect rare target categories by adjusting the weights of positive and negative samples. This study shows that compared with the original model, the improved model has 29% memory compression, a 36.7% reduction in the amount of parameters, and a 4.9% improvement in the detection accuracy, which can effectively improve the detection accuracy of underground helmet wearers, reduce the workload of underground video surveillance personnel, and improve the monitoring efficiency.

Research on lightweight algorithm for gangue detection based on improved Yolov5.

In order to solve the problems of slow detection speed, large number of parameters and large computational volume of deep learning based gangue target detection method, we propose an improved algorithm for gangue target detection based on Yolov5s. First, the lightweight network EfficientVIT is used as the backbone network to increase the target detection speed. Second, C3_Faster replaces the C3 part in the HEAD module, which reduces the model complexity. once again, the 20 × 20 feature map branch in the Neck region is deleted, which reduces the model complexity; thirdly, the CIOU loss function is replaced by the Mpdiou loss function. The introduction of the SE attention mechanism makes the model pay more attention to critical features to improve detection performance. Experimental results show that the improved model size of the coal gang detection algorithm reduces the compression by 77.8%, the number of parameters by 78.3% the computational cost is reduced by 77.8% and the number of frames is reduced by 30.6%, which can be used as a reference for intelligent coal gangue classification.

miR-146a promotes cell migration and invasion in melanoma by directly targeting SMAD4.

Previous studies have explored the functions of microRNA (miR)-146a in different types of cancer through mediating different targets. However, the roles of miR-146a in malignant melanoma (MM) cell migration and invasion remain largely elusive. In the present study, the potential molecular function of miR-146a in MM was investigated. Reverse transcription-quantitative polymerase chain reaction was utilized to detect miR-146a expression in MM tissues and cell lines. A Transwell assay was performed to confirm the ability of migration and invasion. A luciferase assay and biological analysis were used to predict and determine the targets of miR-146a. The expression of miR-146a was upregulated in melanoma tissues and cell lines. Clinicopathological analysis indicated that the miR-146a level was negatively correlated with the progression of melanoma. Abnormal expression of miR-146a promoted cell migration and invasion in MM cells. Additionally, it was also observed that Mothers against decapentaplegic homolog 4 (SMAD4) was a novel target of miR-146a in MM. SMAD4 was negatively associated with miR-146a in MM and abnormal expression of SMAD4 attenuated miR-146a-mediated promotion of cell migration and invasion. In conclusion, miR-146a functioned as an oncogene by directly targeting SMAD4 and it may be a novel diagnostic and therapeutic marker of MM.