Drone detection is a significant research topic due to the potential security threats posed by the misuse of drones in both civilian and military domains. However, traditional drone detection methods are challenged by the drastic scale changes and complex ambiguity during drone flight, and it is difficult to detect small target drones quickly and efficiently. We propose an information-enhanced model based on improved YOLOv5 (TGC-YOLOv5) for fast and accurate detection of small target drones in complex environments. The main contributions of this paper are as follows: First, the Transformer encoder module is incorporated into YOLOv5 to augment attention toward the regions of interest. Second, the Global Attention Mechanism (GAM) is embraced to mitigate information diffusion among distinct layers and amplify the global cross-dimensional interaction features. Finally, the Coordinate Attention Mechanism (CA) is incorporated into the bottleneck part of C3, enhancing the extraction capability of local information for small targets. To enhance and verify the robustness and generalization of the model, a small target drone dataset (SUAV-DATA) is constructed in all-weather, multi-scenario, and complex environments. The experimental results show that based on the SUAV-DATA dataset, the AP value of TGC-YOLOv5 reaches 0.848, which is 2.5% higher than the original YOLOv5, and the Recall value of TGC-YOLOv5 reaches 0.823, which is a 3.8% improvement over the original YOLOv5. The robustness of our proposed model is also verified on the Real-World open-source image dataset, achieving the best accuracy in light, fog, stain, and saturation pollution images. The findings and methods of this paper have important significance and value for improving the efficiency and precision of drone detection.

Fault diagnosis based on vibration signals is widely used in various industrial production processes to prevent catastrophic accidents and ensure timely repairs. However, traditional fault diagnosis methods provide limited accuracy due to their inability to visually represent fault characteristics and heavy reliance on expert involvement. In this study, we propose a time series image generation scheme that incorporates a convolutional neural network to perform data-driven analysis and open-set classification on fault data to represent faults intuitively and diagnose them more accurately. In this study, based on the basic G image, we proposed two series of extended coding methods, G-TF and TF-G to convert the one-dimensional time series signal into a grayscale image from different perspective for figurative expression, so as to adapt to different fault diagnosis scenarios. Then, a convolutional neural network is designed to maximize recognition accuracy based on faster calculation. To demonstrate the effectiveness of the proposed method, three types of faults are tested in this study, i.e., faults with motor bearings, self-priming centrifugal pumps, and hydraulic pumps. The experimental results demonstrate that, when the most suitable method is employed, the proposed approach achieves better recognition performance in diagnosing these three types of faults. The proposed method provides a new solution for future fault diagnosis tasks.