ABSTRACT
This article presents the details of Historical-crack18-19 dataset containing around 3886 annotated concrete surface images from historical buildings. The dataset comprises about 40 raw images collected from an ancient mosque (Masjid) in Historic Cairo, Egypt, with about 757 cracked and 3139 non-cracked surface instances. The images of Historical-crack18-19 dataset were captured using Canon EOS REBEL T3i digital camera with 5184 × 3456 resolution over two years (2018 and 2019). The images of Historical-crack18-19 dataset are annotated with the help of an expert and are intended for training and validation of automated non-invasive crack detection and crack severity recognition as well as crack segmentation approaches based on Machine learning (ML) and Deep Learning (DL) models. According to the environmental circumstances, where the dataset was collected, several challenges are encountered by crack detection/segmentation systems in surface images of historical buildings (illumination, crack-like patterns, separators, dust, blurring, deep texture, etc.). Further, researchers can use the dataset for benchmarking the performance of state-of-the-art methods designed for solving related (image classification and object detection problems. Historical-crack18-19 dataset is freely available at [https://data.mendeley.com/datasets/xfk99kpmj9/1].
ABSTRACT
Museum contents are vulnerable to bad ambience conditions and human vandalization. Preserving the contents of museums is a duty towards humanity. In this paper, we develop an Internet of Things (IoT)-based system for museum monitoring and control. The developed system does not only autonomously set the museum ambience to levels that preserve the health of the artifacts and provide alarms upon intended or unintended vandalization attempts, but also allows for remote ambience control through authorized Internet-enabled devices. A key differentiating aspect of the proposed system is the use of always-on and power-hungry sensors for comprehensive and precise museum monitoring, while being powered by harvesting the Radio Frequency (RF) energy freely available within the museum. This contrasts with technologies proposed in the literature, which use RF energy harvesting to power simple IoT sensing devices. We use rectenna arrays that collect RF energy and convert it to electric power to prolong the lifetime of the sensor nodes. Another important feature of the proposed system is the use of deep learning to find daily trends in the collected environment data. Accordingly, the museum ambience is further optimized, and the system becomes more resilient to faults in the sensed data.
