Unmanned aerial vehicle (UAV) aerial image object detection is a valuable and challenging research field. Despite the breakthrough of deep learning-based object detection networks in natural scenes, UAV images often exhibit characteristics such as a high proportion of small objects, dense distribution, and significant variations in object scales, posing great challenges for accurate detection. To address these issues, we propose an innovative multi-scale feature fusion small object detection network (MFFSODNet). First, concerning the high proportion of small objects in UAV images, an additional tiny object prediction head is introduced instead of the large object prediction head. This approach provides a good detection accuracy of small objects and significantly reduces the parameters. Secondly, to enhance the feature extraction capability of the network for fine-grained information from small objects, a multi-scale feature extraction module (MSFEM) is designed, which could extract rich and valuable multi-scale feature information through convolution operation of different scales on multiple branches. Thirdly, to fuse the fine-grained information from shallow feature maps and the semantic information from deep feature maps, a new bidirectional dense feature pyramid network (BDFPN) is proposed. By expanding the feature pyramid network scale and introducing skip connections, BDFPN achieves efficient multi-scale information fusion. Extensive experiments on the VisDrone and UAVDT benchmark datasets demonstrate that MFFSODNet outperforms state-of-the-art object detection methods and further validate the effectiveness and generalization of MFFSODNet on photovoltaic array defect datasets (PVD).

Intrusion detection is the core topic of network security, and the intrusion detection algorithm based on deep learning has become a research hotspot in network security. In this paper, a network intrusion detection classification model (NIDS-CNNLSTM) based on deep learning is constructed for the wireless sensing scenario of the Industrial Internet of Things (IIoT) to effectively distinguish and identify network traffic data and ensure the security of the equipment and operation of the IIoT. NIDS-CNNLSTM combines the powerful learning ability of long short-term memory neural networks in time series data, learns and classifies the features selected by the convolutional neural network, and verifies the applicability based on binary classification and multi-classification scenarios. The model is trained using KDD CUP99, NSL_KDD, and UNSW_NB15 classic datasets. The verification accuracy and training loss on the three datasets all show good convergence and level, and the accuracy rate is high when classifying various types of traffic. The overall performance of NIDS-CNNLSTM has been significantly improved compared with the models proposed in previous studies. The effectiveness shows a high detection rate and classification accuracy and a low false alarm rate through experimental results. It is more suitable for large-scale and multi-scenario network data in the IIoT.

Aluminum Profiles (APs) are aluminum materials obtained by hot melting and extruding aluminum rods. It has the characteristics of low cost, strong plasticity, easy processing, and recyclability, and therefore plays an important role in industrial production. However, defects such as Non-Conductive (NC), Scratch, Orange Peel (OP), and Dirty Point (DP) often occur during the production and processing of APs, which can seriously affect the quality of APs. In addition, surface defects of APs also have problems such as fuzzy regional definition, large-scale variation, imbalance of aspect ratio, and high inter-class defect similarity, making defect detection more challenging. To solve these problems, this paper proposes an attention-guided object detection algorithm called MA-YOLO, specifically for Surface Defect Detection (SDD) of APs. The algorithm is based on YOLOv5s. First, the K-Means++ clustering algorithm is used to optimize the anchor boxes, which alleviates the problem of aspect ratio imbalance. Then, by improving the multi-scale Feature Fusion Network (FFN), the detection performance of the model to detect the defects with unbalanced aspect ratio is improved, and the adaptability of the model to defects of different scales is enhanced. Finally, a novel Max Pooling Average Pooling (MA) attention module is proposed to improve the overall detection performance of the model, especially for small-scale defects. Experimental results on the aluminum profile surface defect dataset show that MA-YOLO has better detection performance and superiority than the current mainstream object detection algorithms, and compared with the baseline YOLOv5s, the mAP ₅₀ and F1 score are increased by 2.9% and 2.2%, respectively, while keeping the model lightweight. This indicates that MA-YOLO has broad application prospects in the surface defect detection of APs.

With the rapid growth of Internet of Vehicles (IoV) technology, the performance and privacy of IoV terminals (IoVT) have become increasingly important. This paper proposes a federated learning model for IoVT classification using connection records (FLM-ICR) to address privacy concerns and poor computational performance in analyzing users' private data in IoV. FLM-ICR, in the horizontally federated learning client-server architecture, utilizes an improved multi-layer perceptron and logistic regression network as the model backbone, employs the federated momentum gradient algorithm as the local model training optimizer, and uses the federated Gaussian differential privacy algorithm to protect the security of the computation process. The experiment evaluates the model's classification performance using the confusion matrix, explores the impact of client collaboration on model performance, demonstrates the model's suitability for imbalanced data distribution, and confirms the effectiveness of federated learning for model training. FLM-ICR achieves the accuracy, precision, recall, specificity, and F1 score of 0.795, 0.735, 0.835, 0.75, and 0.782, respectively, outperforming existing research methods and balancing classification performance and privacy security, making it suitable for IoV computation and analysis of private data.