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An improved YOLOv8 detection method is proposed for detecting distracted driving behavior and driver’s emotion. Unlike the commonly used YOLOv8 method, an attention mechanism named MHSA and a CNN module are synthesized to ensure improved performance in terms of accuracy and convergence, where MHSA is used to detect distracted driving behavior and CNN is used to detect driver’s emotion. The FER2013 dataset and collected dataset are used to train the improved YOLOv8. The training results show that the proposed YOLOv8 demonstrates improved performance compared with the commonly used YOLO based methods. Finally, the validity of the proposed YOLOv8 method is illustrated through implementations in Jetson Nano platform, where the TensorRT and DeepStream methods in the Jetson Nano device are used to optimize the volume and operational speed of the proposed YOLOv8 method, respectively. Test results show that the proposed YOLOv8 method can yield better real-time and accuracy properties.

Chromosomal karyotype is important to determine whether a newborn has a genetic disorder. There are two main categories of chromosomal abnormalities: structural abnormalities, in which the chromosome structure is altered, and chromosome number abnormalities. Manual karyotyping is complex and takes a lot of time because it requires a high degree of domain expertise. Based on this investigation, we propose a new method of chromosome defect detection based on deep learning with 20,299 chromosome images from Dongguan Kanghua Hospital as data that integrates the diversity of chromosome features and trains a classifier model based on feature fusion for chromosome abnormality detection. We put forward a feature fusion classifier with dynamic weights (FFCDW) for chromosomal abnormality detection, after data augmentation with three deep learning networks, ResNet, SENet, and VGG19, the three trained models are combined using a dynamic weighting approach. Experiments prove the FFCDW method outperforms these mainstream models of ResNet, SENet, and VGG19. The proposed method based on FFCDW achieves a precision of 0.8902 and an F1-score of 0.8805 with a small standard deviation (0.00903 and 0.00892, respectively). In addition, the algorithm can automatically assign weights based on the results of a single model, and the strategy with dynamic weights outperforms the strategy with fixed weights in the proposed feature fusion classifier.

In recent years, with the rapid development of artificial intelligence, machine vision technology has been widely used in various fields. Traditional cancer detection methods are time-consuming, labor-intensive, and highly dependent on the experience of pathologists; therefore, these methods cannot meet the needs of modern medical treatment. Machine vision overcomes the disadvantages of traditional detection methods in cancer detection and can help pathologists improve the detection accuracy. According to the requirements of medical detection, this review summarizes the applications of machine vision in the detection of cancer cells in histopathological images and analyzes the advantages and disadvantages of existing methods in image preprocessing, segmentation, feature extraction and recognition. Finally, research on the detection methods of histopathological cancer cells is reviewed and prospected, and future development trends are predicted to provide guidance for follow-up research.