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.

The emerging technology of Structural Health Monitoring (SHM) paved the way for spotting and continuous tracking of structural damage. One of the major defects in historical structures is cracking, which represents an indicator of potential structural deterioration according to its severity. This paper presents a novel crack severity recognition system using a hybrid filter-wrapper with multi-objective optimization feature selection method. The proposed approach comprises two main components, namely, (1) feature extraction based on hand-crafted feature engineering and CNN-based deep feature learning and (2) feature selection using hybrid filter-wrapper with a multi-objective improved salp swarm optimization. The proposed approach is trained and validated by utilizing 10 representative UCI datasets and 4 datasets of crack images. The obtained experimental results show that the proposed system enhances the performance of crack severity recognition with ≈ 37% and ≈ 31% increase in recognition average accuracy and F-measure, respectively. Also, a reduction rate of ≈ 67% is achieved in the extracted feature set with all the tested datasets compared to the conventional classification approaches using the whole set of features. Moreover, the proposed approach outperforms other approaches with classical feature selection methods in terms of feature reduction rate and computational time. It is noticed that using VGG16 learned features outperforms using the fused hand-crafted features by 17.7%, 15.9%, and 23.5% for fine, moderate, and severe crack recognition, respectively. The significance of this paper is to investigate and highlight the impact of applying multi-feature dimensionality reduction through adopting hybrid filter-wrapper with multi-objective optimization methods for feature selection considering the case study of crack severity recognition for SHM.

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].

Strawberry (Fragaria X ananassa) is one of the most popular fruits cultivated around the world. That is owing to its unique flavor and nutritious properties in addition to the wide usage utility in fresh or processed condition. Strawberry fruits also have a significant economic importance around the world with strong potential as an export commodity. As a matter of fact, investigation and assessment of various strawberry fruit characteristics at different developmental stages is crucial for candidate cultivars selection in fruit plantation as well as fruit yield prediction. Strawberry fruits developmental stage is conventionally applied visually based on expert knowledge, which is a time and labor exhaustive process. Thus, this paper presents a dataset, namely Strawberry-DS (Strawberry-Developmental Stages) dataset, consisting of strawberry fruits (Festival CV type) expert-annotated images at various developmental stages. Data collection was performed on site during the period between January and March from a greenhouse located in the Central Laboratory for Agricultural Climate (CLAC) at the Agricultural Research Center of the Ministry of Agriculture and Land Reclamation in Giza, Egypt. The dataset comprises 247 high-resolution RGB (.jpg) images annotated manually, using Roboflow Annotate annotation tool with reference to ground truth Region of Interest (RoI), and presented in YOLO (.txt files) format. The presented Strawberry-DS dataset can be generally used for developing various automated models of strawberry fruits detection, fruits maturity stage classification, as well as visual counting, through taking into account the visual features such as shape, color, size, and texture of strawberry fruits. Strawberry-DS is freely available at: https://data.mendeley.com/datasets/z6dtfdpzz8/1.

Crack detection on historical surfaces is of significant importance for credible and reliable inspection in heritage structural health monitoring. Thus, several object detection deep learning models are utilized for crack detection. However, the majority of these models are powerful at most in achieving the task of classification, with primitive detection of the crack location. On the other hand, several state-of-the-art studies have proven that pixel-level crack segmentation can powerfully locate objects in images for more accurate and reasonable classification. In order to realize pixel-level deep crack segmentation in images of historical buildings, this paper proposes an automated deep crack segmentation approach designed based on an exhaustive investigation of several U-Net deep learning network architectures. The utilization of pixel-level crack segmentation with U-Net deep learning ensures the identification of pixels that are important for the decision of image classification. Moreover, the proposed approach employs the deep learned features extracted by the U-Net deep learning model to precisely describe crack characteristics for better pixel-level crack segmentation. A primary image dataset of various crack types and severity is collected from historical building surfaces and used for training and evaluating the performance of the proposed approach. Three variants of the U-Net convolutional network architecture are considered for the deep pixel-level segmentation of different types of cracks on historical surfaces. Promising results of the proposed approach using the $U^2-Net$ deep learning model are obtained, with a Dice score and mean Intersection over Union (mIoU) of 71.09% and 78.38% achieved, respectively, at the pixel level. Conclusively, the significance of this work is the investigation of the impact of utilizing pixel-level deep crack segmentation, supported by deep learned features, through adopting variants of the U-Net deep learning model for crack detection on historical surfaces.