Maintaining roadway infrastructure is essential for ensuring a safe, efficient, and sustainable transportation system. However, manual data collection for detecting road damage is time-consuming, labor-intensive, and poses safety risks. Recent advancements in artificial intelligence, particularly deep learning, offer a promising solution for automating this process using road images. This paper presents a comprehensive workflow for road damage detection using deep learning models, focusing on optimizations for inference speed while preserving detection accuracy. Specifically, to accommodate hardware limitations, large images are cropped, and lightweight models are utilized. Additionally, an external pothole dataset is incorporated to enhance the detection of this underrepresented damage class. The proposed approach employs multiple model architectures, including a custom YOLOv7 model with Coordinate Attention layers and a Tiny YOLOv7 model, which are trained and combined to maximize detection performance. The models are further reparameterized to optimize inference efficiency. Experimental results demonstrate that the ensemble of the custom YOLOv7 model with three Coordinate Attention layers and the default Tiny YOLOv7 model achieves an F1 score of 0.7027 with an inference speed of 0.0547 seconds per image. The complete pipeline, including data preprocessing, model training, and inference scripts, is publicly available on the project's GitHub repository, enabling reproducibility and facilitating further research.

Forecasting GDP is a highly practical task in macroeconomics, especially in the context of rapidly changing economic environments caused by both economic and non-economic factors. This study proposes a deep learning model that integrates Long Short-Term Memory (LSTM) networks with a phase-adaptive attention mechanism (PAA-LSTM model) to improve forecasting accuracy. The attention mechanism is flexibly adjusted according to different phases of the economic cycle—recession, recovery, expansion, and stagnation—allowing the model to better capture temporal dynamics compared to traditional static attention approaches. The model is evaluated using GDP data from six countries representing three groups of economies: developed, emerging, and developing. The experimental results show that the proposed model achieves superior accuracy in countries with strong cyclical structures and high volatility. In more stable economies, such as the United States and Canada, PAA-LSTM remains competitive; however, its margin over simpler models is narrower, suggesting that the benefits of added complexity may vary depending on economic structure. These findings underscore the value of incorporating economic cycle phase information into deep learning models for macroeconomic forecasting and suggest a promising direction for selecting flexible forecasting architectures tailored to different country groups.