This paper provides a comprehensive study of the security of YOLO (You Only Look Once) model series for object detection, emphasizing their evolution, technical innovations, and performance across the COCO dataset. The robustness of YOLO models under adversarial attacks and image corruption, offering insights into their resilience and adaptability, is analyzed in depth. As real-time object detection plays an increasingly vital role in applications such as autonomous driving, security, and surveillance, this review aims to clarify the strengths and limitations of each YOLO iteration, serving as a valuable resource for researchers and practitioners aiming to optimize model selection and deployment in dynamic, real-world environments. The results reveal that YOLOX models, particularly their large variants, exhibit superior robustness compared to other YOLO versions, maintaining higher accuracy under challenging conditions. Our findings serve as a valuable resource for researchers and practitioners aiming to optimize YOLO models for dynamic and adversarial real-world environments while guiding future research toward developing more resilient object detection systems.

Christian Orthodox iconography is a fundamental element of the religious cultural heritage of many countries. Iconoclasm, vandalism, and the passage of time ruined the appearance of icons, making it difficult to recognize the depicted saints. This work aims to test the performance of 13 state-of-the-art deep learning models for the task of Christian Orthodox saints’ recognition from images of preserved wooden hand-painted icons, which has never before been reported in the literature. Additionally, this work introduces the first public image dataset (ICONSAINT—ICONographic SAINT Recognition Dataset) of saint icons for classification tasks, including 2730 annotated images of 546 icons of 123 classes. All models were tested in three experimental setups, involving a balanced part of the dataset of six classes, an imbalanced part of the dataset of 12 classes and a medium-imbalanced part of the dataset of eight classes, reporting accuracy of up to 89% with VGG19 for the balanced data, of up to 78% for MobileNet with the imbalanced data, and of up to 87% with DenseNet201 for the medium-imbalanced data. Moreover, Class Activation Maps (CAMs) were considered to highlight the regions of the input image that mostly influenced the decision of the models towards adding valuable explainability to the results through visual explanations.

Background/Objectives: Falls among the older adult population represent a significant public health concern, often leading to diminished quality of life and serious injuries that escalate healthcare costs, and they may even prove fatal. Accurate fall risk prediction is therefore crucial for implementing timely preventive measures. However, to date, there is no definitive metric to identify individuals with high risk of experiencing a fall. To address this, the present study proposes a novel approach that transforms biomechanical time-series data, derived from gait analysis, into visual representations to facilitate the application of deep learning (DL) methods for fall risk assessment. Methods: By leveraging convolutional neural networks (CNNs) and Siamese neural networks (SNNs), the proposed framework effectively addresses the challenges of limited datasets and delivers robust predictive capabilities. Results: Through the extraction of distinctive gait-related features and the generation of class-discriminative activation maps using Grad-CAM, the random forest (RF) machine learning (ML) model not only achieves commendable accuracy (83.29%) but also enhances explainability. Conclusions: Ultimately, this study underscores the potential of advanced computational tools and machine learning algorithms to improve fall risk prediction, reduce healthcare burdens, and promote greater independence and well-being among the older adults.