With the rapid development and increasing demands of civil aviation, the accurate detection of key aircraft components and staff on airport aprons is of great significance for ensuring the safety of flights and improving the operational efficiency of airports. However, the existing detection models for airport aprons are relatively scarce, and their accuracy is insufficient. Based on YOLOv5, we propose an improved object detection algorithm, called DT-YOLO, to address these issues. We first built a dataset called AAD-dataset for airport apron scenes by randomly sampling and capturing surveillance videos taken from the real world to support our research. We then introduced a novel module named D-CTR in the backbone, which integrates the global feature extraction capability of Transformers with the limited receptive field of convolutional neural networks (CNNs) to enhance the feature representation ability and overall performance. A dropout layer was introduced to reduce redundant and noisy features, prevent overfitting, and improve the model’s generalization ability. In addition, we utilized deformable convolutions in CNNs to extract features from multi-scale and deformed objects, further enhancing the model’s adaptability and detection accuracy. In terms of loss function design, we modified GIoULoss to address its discontinuities and instability in certain scenes, which effectively mitigated gradient explosion and improved the stability of the model. Finally, experiments were conducted on the self-built AAD-dataset. The results demonstrated that DT-YOLO significantly improved the mean average precision (mAP). Specifically, the mAP increased by 2.6 on the AAD-dataset; moreover, other metrics also showed a certain degree of improvement, including detection speed, AP50, AP75, and so on, which comprehensively proves that DT-YOLO can be applied for real-time object detection in airport aprons, ensuring the safe operation of aircraft and efficient management of airports.

As a fundamental and indispensable component of modern electronic devices, the printed circuit board (PCB) has a complex structure and highly integrated functions, with its manufacturing quality directly affecting the stability and reliability of electronic products. However, during large-scale automated PCB production, its surfaces are prone to various defects and imperfections due to uncontrollable factors, such as diverse manufacturing processes, stringent machining precision requirements, and complex production environments, which not only compromise product functionality but also pose potential safety hazards. At present, PCB defect detection in industry still predominantly relies on manual visual inspection, the efficiency and accuracy of which fall short of the automation and intelligence demands in modern electronics manufacturing. To address this issue, in this paper, we have made improvements based on the classical Faster-RCNN object detection framework. Firstly, ResNet-101 is employed to replace the conventional VGG-16 backbone, thereby enhancing the ability to perceive small objects and complex texture features. Then, we extract features from images by using deformable convolution in the backbone network to improve the model’s adaptive modeling capability for deformed objects and irregular defect regions. Finally, the Convolutional Block Attention Module is incorporated into the backbone, leveraging joint spatial and channel attention mechanisms to improve the effectiveness and discriminative power of feature representations. The experimental results demonstrate that the improved model achieves a 4.5% increase in mean average precision compared with the original Faster-RCNN. Moreover, the proposed method exhibits superior detection accuracy, robustness, and adaptability compared with mainstream object detection models, indicating strong potential for engineering applications and industrial deployment.