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.

Unmanned aerial vehicles (UAVs) have been widely used in aerial photography and target detection tasks due to their flexibility and unique perspective. However, small targets often suffer from insufficient resolution, uneven scale distribution, and complex background clutter, which are constrained by imaging conditions such as high-altitude imaging, long-distance capture, and wide field of view. These factors weaken the feature representation and generalization ability of the model, becoming the key bottleneck that restricts the improvement of small target detection accuracy in UAV scenarios. To address the above issues, this paper proposes a small target detection algorithm for UAV perspective, namely MTD-YOLO. First, a Parallel Multi-Scale Receptive Field Unit (PMSRFU) is designed. This unit effectively enhances the receptive field range of feature extraction and the fusion ability of multi-scale contextual information by introducing parallel branches with different-sized convolutional kernels. Second, we embed PMSRFU into a C2f block to form C2f-PMSRFU, which reuses shallow details and fuses multi-scale features to clarify edges and textures in small targets, yielding stronger fine-grained representations. Finally, an efficient detection head with task decoupling, dynamic alignment, and adaptive scale adjustment capabilities, namely SDIDA-Head, is proposed, which significantly improves the model’s small target detection accuracy. Extensive experiments on the VisDrone2019 and HazyDet datasets demonstrate that MTD-YOLO achieves a 7.6% and 6.6% increase in mAP@0.5 compared to the baseline YOLOv8n, respectively. Meanwhile, the Precision is improved by 6.0% and 1.1%, and the Recall is enhanced by 7.5% and 6.9%, respectively. These results fully validate the effectiveness and superiority of the proposed method in UAV small target detection tasks.

Remote sensing infrared ship detection is crucial for maritime safety and traffic management in civil-military integrated applications. However, the detection accuracy and robustness of current methods are still limited by dataset constraints, including small scale, narrow target size distribution, sparse targets, and high scenario specificity. To address these problems, we integrate publicly available datasets to construct IRShip—a relatively large-scale infrared ship detection dataset comprising 27,138 images, significantly improving data scale and diversity. We further design a copy-poisson blend (CP-PB) offline data augmentation approach and introduce a dense one-to-one (Dense-O2O) online augmentation strategy, which improve adaptability to complex backgrounds and scale variations, mitigate problems from sparse targets, and improve dataset utility and model robustness. We propose MSCK-Net, a multiscale chinese knot convolutional network tailored for detecting dim and small infrared ship targets (DSIRST). Specifically, we propose a novel Chinese Knot Convolution (CKConv) that better aligns with both the local features of small targets and the morphological characteristics of ship targets, thereby significantly enhancing low-level feature representation. Built with CKConv, the multiscale knot block (MSK-Block) and Stem-ck modules enhance deep feature transmission efficiency and global modeling of DSIRST, leading to notable gains in detection performance. Extensive experiments on IRShip, NUDT-SIRST-Sea, ISDD, IRSDSS, and Maritime-sirst demonstrate that MSCK-Net-M achieves state-of-the-art performance, with AP₅₀, AP₇₅, and AP of 82.5%, 53.1%, and 50.9% on IRShip, significantly outperforming the existing 20 general object detectors and 6 infrared target detectors. Generalization experiments on four sub-datasets further verify the effectiveness and robustness of our proposed method. The code and dataset are available at: https://github.com/sjxxrh/MSCK-Net.

In the past decade, object detection technology has developed rapidly. However, in the field of unmanned aerial vehicle (UAV) image object detection, challenges such as complex environments, numerous and dense small objects, and weak features make object detection from the UAV perspective a highly challenging task. To address these issues, this letter proposes a U-Shaped feature extraction and fusion network (U-ShapeNet). Specifically: first, to enhance the network’s feature extraction capability and improve the perception of small objects, we design a novel U-Shaped feature extraction network (U-SFEN) and introduce a tiny object detection head. Second, a large kernel feature selection module (LKFSM) is constructed to strengthen the network’s contextual information learning ability and effectively distinguish small objects from complex background noise. Third, a same-scale feature enhancement module (SFEM) is proposed to mitigate information decay by reusing same-scale feature maps. Experiments on the VisDrone2019 and HazyDet datasets demonstrate that U-ShapeNet outperforms current mainstream object detectors, achieving state-of-the-art performance.