The concept of the “smart city” has emerged with the advancement of technology, but some facilities are not sufficiently intelligent, such as parking lots. Hence, this paper proposes an inexpensive and plug-to-play camera-based smart parking system for airports. The system utilizes inverse perspective mapping (IPM) to provide an aerial view image of the parking lot, which is then processed to extract parking space information. The system also includes a guidance system to assist drivers in finding available parking spaces. The system is simulated on a 3D scene based on the parking lot of Macao International Airport. In the experiment, our system achieved an accuracy rate of 97.03% and a mean distance error of 8.59 pixels. This research study shows the potential of enhancing parking lots using only cameras as data collectors, and the results show that the system is capable of providing accurate and useful information. It performs well in parking lots with open space, in particular. Moreover, it is an economical solution for implementing a smart parking lot.

Aiming at the inconsistency of manual detection of mobile phone screen defects, the image feature extraction of traditional machine learning is often set based on experience, resulting in unsatisfactory detection results. Therefore, a mobile phone screen defect detection model (Ghostbackbone) which is proposed by this paper based on YOLOv5 s and Ghostbottleneck. The bottleneck of Ghostbackbone mainly uses and improves the Ghostbottleneck of GhostNet. The attention module of Ghostbackbone uses Coordinated Attention and Depthwise Separable Convolution for parameter reduction. Finally, Ghostbackbone uses YOLOv5 as the object detector to train the mobile phone screen defect dataset. The experimental results show that the parameter quantity of Ghostbackbone is 24% of that of YOLOv5 s, the average time of detecting a single picture is only 2% lower than that of YOLOv5 s, and the mAP0.5 : 0.95 is 2% higher than that of MobilenetV3 s.

Efficient models capable of handling large numbers of data points in point cloud research are in high demand in computer vision. Despite recent advancements in 3D classification and segmentation tasks in point cloud processing, the deep learning PointNeXt and PointMLP models are plagued with heavy computation requirements with limited efficiencies. In this paper, a novel GhostMLP model for point clouds is thus introduced. It takes the advantages of the GhostNet design modules and uses them to replace the MLP layers in the existing PointMLP model. The resulting GhostMLP architecture achieves superior classification performance with lower computation requirements. Compared to the PointMLP, GhostMLP maintains sustainable performance with fewer parameters and lower FLOPs computations. Indeed, it outperforms PointMLP on the ScanObjectNN dataset, achieving 88.7% overall accuracy and 87.6% mean accuracy with only 6 million parameters and 7.2 GFLOPs—about half the resources required by PointMLP. At the same time, GhostMLP-S is introduced as a lightweight version which also outperforms PointMLP in performance. GhostMLP completes faster training and inference with GPU and is the best-performing method that does not require any extra training data in the ScanObjectNN benchmark. Efficient point cloud analysis is essential in computer vision, and we believe that GhostMLP has the potential to become a powerful tool for large-scale point cloud analysis.

Dual challenges of computational efficiency and representation effectiveness exist in processing point clouds. Inspired by the TDA (Topological Data Analysis), we propose to convert the point cloud to a transposed fully connected and weighted (t-FCW) graph in order to significantly decrease the computational complexity in the following processing steps. We design a TDA pipeline called Point-FCW with a series of vectorization techniques for the 3D object point cloud feature extraction, which is plugged into the non-parametric classification head. Our experimental results demonstrate that Point-FCW achieves 75.28% accuracy on the ModelNet40 dataset with 512 points, providing a tiny, consistent, and effective representation for TDA. Furthermore, when integrated with the state-of-the-art non-parametric network Point-NN, the mixture model performs better, with an improvement of 4.47% in the OBJ-BG split of the ScanObjectNN dataset. Similarly, when integrating Point-FCW into the parametric network, PointMLP yields a performance improvement of 3.54% in the PB-T50-RS split of the ScanObjectNN dataset. The proposed Point-FCW can serve as a complementary enhancement feature when integrated into the Point-NN and PointMLP models. Moreover, the t-FCW graph representation can be efficiently converted at a rate of 3739 samples/second. Our code is available in https://github.com/hawkinglai/Point-FCW.

The arrival of COVID-19 has changed the way traditional classes are conducted, and online teaching has never been more popular. While there are many advantages to online teaching, there are also extremely obvious disadvantages, one of which is the tendency to lack concentration. For this reason, this study uses video images from the DAiSee dataset, a new sampling script, deep learning neural networks, and a new PAD emotion model to systematically assess student concentration. Our test set uses 21 short videos from the DAISee dataset, sampling a total of 1,866 frames. The final results showed that the accuracy of the neural network was approximately 80%. The results of the test set on the PAD model showed that the percentage of attentive listeners was 65.9%, while the percentage of highly inattentive listeners was 6.2%. This study constructed a complete concentration monitoring system for online classrooms centred on smart education which can provide the information of students' concentration in real time.