Aiming at solving the problem that the detection methods used in the existing helmet detection research has low detection efficiency and the cumulative error influences accuracy, a new algorithm for improving YOLOv5 helmet wearing detection is proposed. First of all, we use the K‐means++ algorithm to improve the size matching degree of the a priori anchor box; secondly, integrate the Depthwise Coordinate Attention (DWCA) mechanism in the backbone network, so that the network can learn the weight of each channel independently and enhance the information dissemination between features, thereby strengthening the network’s ability to distinguish foreground and background. The experimental results show as follows: in the self‐made safety helmet wearing detection dataset, the average accuracy rate reached 95.9%, the average accuracy of the helmet detection reached 96.5%, and the average accuracy of the worker’s head detection reached 95.2%. Making a comparison with the YOLOv5 algorithm, our model has a 3% increase in the average accuracy of helmet detection, which is in line with the accuracy requirements of helmet wearing detection in complex construction scenarios.

It is an essential measure for workers to wear safety helmets when entering the construction site to prevent head injuries caused by object collision and falling. This paper proposes a lightweight algorithm for helmet-wearing detection based on YOLOV5, which is faster and more robust for helmet detection in natural construction scenarios. In this paper, the MCA attention mechanism is embedded in the backbone network to help the network extract more productive information, reduce the missed detection rate of small helmet objects and improve detection accuracy. In order to ensure the safety of workers in construction, it is necessary to detect whether the construction workers are wearing safety helmets in real-time to achieve monitoring on-site. A channel pruning strategy is proposed on the MCA-YOLOv5 algorithm to compress it, realizing the optimal large-scale model into ultrasmall models for real-time detection on embedded or mobile devices. The experimental results on the public data set show that the model parameter volume is reduced by 87.2%, and the detection speed is increased by 53.5%, even though the MCA-YOLOv5-light reduces the mAP slightly.

With the increasing popularity of artificial intelligence applications, artificial intelligence technology has begun to be applied in competitive sports. These applications have promoted the improvement of athletes’ competitive ability, as well as the fitness of the masses. Human action recognition technology, based on deep learning, has gradually been applied to the analysis of the technical actions of competitive sports athletes, as well as the analysis of tactics. In this paper, a new graph convolution model is proposed. Delaunay’s partitioning algorithm was used to construct a new spatiotemporal topology which can effectively obtain the structural information and spatiotemporal features of athletes’ technical actions. At the same time, the attention mechanism was integrated into the model, and different weight coefficients were assigned to the joints, which significantly improved the accuracy of technical action recognition. First, a comparison between the current state-of-the-art methods was undertaken using the general datasets of Kinect and NTU-RGB + D. The performance of the new algorithm model was slightly improved in comparison to the general dataset. Then, the performance of our algorithm was compared with spatial temporal graph convolutional networks (ST-GCN) for the karate technique action dataset. We found that the accuracy of our algorithm was significantly improved.

Detecting students’ classroom behaviors from instructional videos is important for instructional assessment, analyzing students’ learning status, and improving teaching quality. To achieve effective detection of student classroom behavior based on videos, this paper proposes a classroom behavior detection model based on the improved SlowFast. First, a Multi-scale Spatial-Temporal Attention (MSTA) module is added to SlowFast to improve the ability of the model to extract multi-scale spatial and temporal information in the feature maps. Second, Efficient Temporal Attention (ETA) is introduced to make the model more focused on the salient features of the behavior in the temporal domain. Finally, a spatio-temporal-oriented student classroom behavior dataset is constructed. The experimental results show that, compared with SlowFast, our proposed MSTA-SlowFast has a better detection performance with mean average precision (mAP) improvement of 5.63% on the self-made classroom behavior detection dataset.

Precisely segmenting the hippocampus from the brain is crucial for diagnosing neurodegenerative illnesses such as Alzheimer’s disease, depression, etc. In this research, we propose an enhanced hippocampus segmentation algorithm based on 3D U-Net that can significantly increase hippocampus segmentation performance. First, a dynamic convolution block is designed to extract information more comprehensively in the steps of the 3D U-Net’s encoder and decoder. In addition, an improved coordinate attention algorithm is applied in the skip connections step of the 3D U-Net to increase the weight of the hippocampus and reduce the redundancy of other unimportant location information. The algorithm proposed in this work uses soft pooling methods instead of max pooling to reduce information loss during downsampling steps. The datasets employed in this research were obtained from the MICCAI 2013 SATA Challenge (MICCAI) and the Harmonized Protocol initiative of the Alzheimer’s Disease Neuroimaging Initiative (HarP). The experimental results on the two datasets prove that the algorithm proposed in this work outperforms other commonly used segmentation algorithms. On the HarP, the dice increase by 3.52%, the mIoU increases by 2.65%, and the F1 score increases by 3.38% in contrast to the baseline. On the MICCAI, the dice, the mIoU, and the F1 score increase by 1.13%, 0.85%, and 1.08%, respectively. Overall, the proposed model outperforms other common algorithms.