In recent years, with the rapid development of tourism, the abnormal events in the traffic fortress of tourist attractions not only endanger personal safety, but also cause a lot of negative effects on the society. In the face of the low accuracy of abnormal event detection in complex scenes and the low efficiency of manual observation, combining the advantages and characteristics of YOLOv5 and 3D convolutional neural network(C3D), an automatic abnormal event detection algorithm based on YOLOv5+C3D was proposed. Experimental data show that compared with other methods, the abnormal event detection method based on YOLOv5+C3D has a higher accuracy for various abnormal events detection, indicating that the trained model has a strong generalization ability for abnormal event detection.

In order to solve the problems of low signal-to-noise ratio, uncertain position, and blurred edges of mammography images, and to achieve full-view breast mass segmentation, a breast mass segmentation network based on improved UNet 3+ is proposed. The network provides prior knowledge of edge information for model segmentation by adding an edge-aware module and utilizing shallow and deep features in the network. At the same time, Hybrid loss function (HAM) is added to the network to obtain rich multi-scale information for handling lumps of different sizes and shapes. In addition, we add Dice loss and bce loss to the hybrid loss function to address the problem of pixel class imbalance in mammography images. The experimental results show that the improved UNet 3+ segmentation model has reached the Dice scores of 85.89% and 82.55% on the CBIS-DDSM and INbreast datasets, has improved aa and bb compared with the previous ones, and has a higher performance in the breast mass segmentation task. The accuracy rate is better than other classical models.

A lightweight object detection algorithm based on YOLOv5 is proposed to address the problem of deploying detection models for traffic targets. This method was proposed to reduce the number of channels in the backbone and introduced GSConv to improve performance. At the same time, GSConv was improved to cut the parameters. The C3 module was replaced in the Neck with C2f to obtain more comprehensive gradient flow information. Finally, the model parameters are only 1.41M. Experiment on BDD100K public traffic dataset shows that the lightweight model reduces the number of parameters while losing a small amount of Recalls, and its performance is better than mainstream lightweight networks.

In certain industries such as construction, high risks are often associated with the construction process, and safety helmets are crucial protective gear for workers on construction sites. To address the issues of missed and false detections of safety helmets in complex environments with current helmet detection methods, we propose an improved YOLOv5 object detection algorithm to detect the wearing of safety helmets. The proposed improvements include the addition of an attention mechanism and replacement of the loss function. The proposed method adds an efficient channel attention (ECA) mechanism to the YOLOv5 head network to enhance the model's ability to extract features related to safety helmets, thereby increasing precision without adding too much computational complexity. The loss function is replaced with NWD to effectively improve the detection progress of small safety helmets in YOLOv5.