SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

Rock Crack Recognition Technology Based on Deep Learning

The changes in cracks on the surface of rock mass reflect the development of geological disasters, so cracks on the surface of rock mass are early signs of geological disasters such as landslides, collapses, and debris flows. To research geological disasters, it is crucial to swiftly and precisely gather crack information on the surface of rock masses. Drone videography surveys can effectively avoid the limitations of the terrain. This has become an essential method in disaster investigation. This manuscript proposes rock crack recognition technology based on deep learning. First, images of cracks on the surface of a rock mass obtained by a drone were cut into small pictures of 640 × 640. Next, a VOC dataset was produced for crack object detection by enhancing the data with data augmentation techniques, labeling the image using Labelimg. Then, we divided the data into test sets and training sets in a ratio of 2:8. Then, the YOLOv7 model was improved by combining different attention mechanisms. This study is the first to combine YOLOv7 and an attention mechanism for rock crack detection. Finally, the rock crack recognition technology was obtained through comparative analysis. The results show that the precision of the improved model using the SimAM attention mechanism can reach 100%, the recall rate can achieve 75%, the AP can reach 96.89%, and the processing time per 100 images is 10 s, which is the optimal model compared with the other five models. The improvement is relative to the original model, in which the precision was improved by 1.67%, the recall by 1.25%, and the AP by 1.45%, with no decrease in running speed. This proves that rock crack recognition technology based on deep learning can achieve rapid and precise results. It provides a new research direction for identifying early signs of geological hazards.

3D face recognition algorithm based on deep Laplacian pyramid under the normalization of epidemic control

Under the normalization of epidemic control in COVID-19, it is essential to realize fast and high-precision face recognition without feeling for epidemic prevention and control. This paper proposes an innovative Laplacian pyramid algorithm for deep 3D face recognition, which can be used in public. Through multi-mode fusion, dense 3D alignment and multi-scale residual fusion are ensured. Firstly, the 2D to 3D structure representation method is used to fully correlate the information of crucial points, and dense alignment modeling is carried out. Then, based on the 3D critical point model, a five-layer Laplacian depth network is constructed. High-precision recognition can be achieved by multi-scale and multi-modal mapping and reconstruction of 3D face depth images. Finally, in the training process, the multi-scale residual weight is embedded into the loss function to improve the network's performance. In addition, to achieve high real-time performance, our network is designed in an end-to-end cascade. While ensuring the accuracy of identification, it guarantees personnel screening under the normalization of epidemic control. This ensures fast and high-precision face recognition and establishes a 3D face database. This method is adaptable and robust in harsh, low light, and noise environments. Moreover, it can complete face reconstruction and recognize various skin colors and postures.

Application of Improved YOLOv5 in Aerial Photographing Infrared Vehicle Detection

Aiming to solve the problems of false detection, missed detection, and insufficient detection ability of infrared vehicle images, an infrared vehicle target detection algorithm based on the improved YOLOv5 is proposed. The article analyzes the image characteristics of infrared vehicle detection, and then discusses the improved YOLOv5 algorithm in detail. The algorithm uses the DenseBlock module to increase the ability of shallow feature extraction. The Ghost convolution layer is used to replace the ordinary convolution layer, which increases the redundant feature graph based on linear calculation, improves the network feature extraction ability, and increases the amount of information from the original image. The detection accuracy of the whole network is enhanced by adding a channel attention mechanism and modifying loss function. Finally, the improved performance and comprehensive improved performance of each module are compared with common algorithms. Experimental results show that the detection accuracy of the DenseBlock and EIOU module added alone are improved by 2.5% and 3% compared with the original YOLOv5 algorithm, respectively, and the addition of the Ghost convolution module and SE module alone does not increase significantly. By using the EIOU module as the loss function, the three modules of DenseBlock, Ghost convolution and SE Layer are added to the YOLOv5 algorithm for comparative analysis, of which the combination of DenseBlock and Ghost convolution has the best effect. When adding three modules at the same time, the mAP fluctuation is smaller, which can reach 73.1%, which is 4.6% higher than the original YOLOv5 algorithm.

Mask Wearing Detection Algorithm Based on Improved Tiny YOLOv3

The new coronavirus spreads widely through droplets, aerosols and other carriers. Wearing a mask can effectively reduce the probability of being infected by the virus. Therefore, it is necessary to monitor whether people wear masks in public to prevent the virus from spreading further. However, there is no mature general mask wearing detection algorithm. Based on tiny YOLOv3 algorithm, this paper realizes the detection of face with mask and face without mask, and proposes an improvement to the algorithm. First, the loss function of the bounding box regression is optimized, and the original loss function is optimized as the Generalized Intersection over Union (GIoU) loss. Second, the network structure is improved, the residual unit is introduced into the backbone to increase the depth of the network and the detection of two scales is expanded to three. Finally, the size of anchor boxes is clustered based on [Formula: see text]-means algorithm. The experimental results on the constructed dataset show that, compared with the tiny YOLOv3 algorithm, the algorithm proposed in this paper improves the detection accuracy while maintaining high-speed inference ability.