Defects in residential building façades affect the structural integrity of buildings and degrade external appearances. Defects in a building façade are typically managed using manpower during maintenance. This approach is time-consuming, yields subjective results, and can lead to accidents or casualties. To address this, we propose a building façade monitoring system that utilizes an object detection method based on deep learning to efficiently manage defects by minimizing the involvement of manpower. The dataset used for training a deep-learning-based network contains actual residential building façade images. Various building designs in these raw images make it difficult to detect defects because of their various types and complex backgrounds. We employed the faster regions with convolutional neural network (Faster R-CNN) structure for more accurate defect detection in such environments, achieving an average precision (intersection over union (IoU) = 0.5) of 62.7% for all types of trained defects. As it is difficult to detect defects in a training environment, it is necessary to improve the performance of the network. However, the object detection network employed in this study yields an excellent performance in complex real-world images, indicating the possibility of developing a system that would detect defects in more types of building façades.

Incorporating NiO NTs in perovskite films provided an enhanced grain size and charge extraction, resulting in an improved PCE of 19.3%.

This study proposes a defect detection framework to improve the performance of deep learning-based detection models for ultra-high resolution (UHR) images generated by tunnel inspection systems. Most of the scanning technologies used in tunnel inspection systems generate UHR images. Defects in real-world images, on the other hand, are noticeably smaller than the image. These characteristics make simple preprocessing applications, such as downscaling, difficult due to information loss. Additionally, when a deep learning model is trained by the UHR images under the limited computational resource for training, problems may occur, including a reduction in object detection rate, unstable training, etc. To address these problems, we propose a framework that includes preprocessing and postprocessing of UHR images related to image patches rather than focusing on deep learning models. Furthermore, it includes a method for supplementing problems according to the format of the data annotation in the preprocessing process. When the proposed framework was applied to the UHR images of a tunnel, the performance of the deep learning-based defect detection model was improved by approximately 77.19 percentage points (pp). Because the proposed framework is for general UHR images, it can effectively recognize damage to general structures other than tunnels. Thus, it is necessary to verify the applicability of the defect detection framework under various conditions in future works.

Unpredictable situations frequently occur in real driving environments, and it is often difficult to recognize road signs. In this case, autonomous vehicles (AVs) have a limited ability to predict areas that cannot be detected, making it difficult to judge objects accurately when some information is lost. Therefore, we propose a framework that helps AVs infer proper information under limited conditions. The entire process consists of three steps. First, the missing part of the road sign is restored using the image generative pre-trained transformer model. Next, the sample image with the highest classification accuracy and restored quality is selected among several sample images. Finally, the selected image is provided to users through the designed user interface. The proposed framework improved recognition accuracy compared with unrestored accuracy, indicating the possibility of application as a driving assistance system, and is meaningful in that it is a system that mimics human reasoning ability.