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Aircraft ducts play an indispensable role in various systems of an aircraft. The regular inspection and maintenance of aircraft ducts are of great significance for preventing potential failures and ensuring the normal operation of the aircraft. Traditional manual inspection methods are costly and inefficient, especially under low-light conditions. To address these issues, we propose a new defect detection model called LESM-YOLO. In this study, we integrate a lighting enhancement module to improve the accuracy and recognition of the model under low-light conditions. Additionally, to reduce the model's parameter count, we employ space-to-depth convolution, making the model more lightweight and suitable for deployment on edge detection devices. Furthermore, we introduce Mixed Local Channel Attention (MLCA), which balances complexity and accuracy by combining local channel and spatial attention mechanisms, enhancing the overall performance of the model and improving the accuracy and robustness of defect detection. Finally, we compare the proposed model with other existing models to validate the effectiveness of LESM-YOLO. The test results show that our proposed model achieves an mAP of 96.3%, a 5.4% improvement over the original model, while maintaining a detection speed of 138.7, meeting real-time monitoring requirements. The model proposed in this paper provides valuable technical support for the detection of dark defects in aircraft ducts. [ABSTRACT FROM AUTHOR]

Understanding the determinants of elderly people's public transport usage patterns can offer new insights into elderly mobility issues and provide policy implications for planning toward an aging-friendly and sustainable city. However, few studies have examined the impact of the built environment on the trip time of the elderly using big data. Moreover, the elderly's trip time has been mostly investigated by the multivariate linear regression model (MLR), ignoring the non-linear association between explanatory variables and trip time. Using smart card data from Nanjing in 2019, this study employs a gradient boosting regression trees (GBRT) model to probe into the correlations between the built environment and the elderly's trip time. The results show that significant non-linear relationships exist between trip time and the selected explanatory variables, which cannot be captured by the MLR model. It suggests that relevant policy implementations should be carried out in conjunction with the elderly's travel environment by regarding their threshold effects. Besides, interaction effects of spatial attributes on trip time are identified in our study. For example, elderly people living in the exurban area are more likely to take long-distance metro travel for their physical exercise. These findings demonstrate that planners and policymakers should not only consider one single built-environment factor, but also the interactions of various factors to enhance elderly mobility., Author(s): Zhuangbin Shi [1]; Yang Liu [1]; Mingwei He (corresponding author) [1]; Qiyang Liu [2] 1. Introduction Due to the accelerating global aging process, the social life and well-being of [...]