Predictive maintenance is a key aspect for the safety of critical infrastructure such as bridges, dams, and tunnels, where a failure can lead to catastrophic outcomes in terms of human lives and costs. The surge in Artificial Intelligence-driven visual robotic inspection methods necessitates high-quality datasets containing diverse defect classes with several instances on different conditions (e.g., material, illumination). In this context, we introduce a Controllable Object Inpainting Generative Adversarial Network (COIGAN) to synthetically generate realistic images that augment defect datasets. The effectiveness of the model is quantitatively validated by a Fréchet Inception Distance, which measures the similarity between the generated and training samples. To further evaluate the impact of COIGAN-generated images, a segmentation task was conducted, utilizing key performance metrics such as segmentation accuracy, mAP, mIoU, and F1 score, demonstrating that the synthetic images integrate seamlessly and produce results comparable to real defect images. Subsequently, COIGAN generability was successfully used for the segmentation of a defect-free dataset by inpainting defects. The results showcase COIGAN's ability to learn defect patterns and apply them in new contexts, preserving the original features of the base image and allowing the creation of new datasets with a desired multi-class distribution. Specifically, in the context of predictive maintenance, COIGAN enriches datasets, enabling deep learning models to more effectively identify potential infrastructure anomalies. Project page: https://bit.ly/4bzxwqf.

The fashion industry is undergoing a digital transformation, driven by growing demands for sustainability, personalization and immersive experiences. In this paper, we present Made-In (Multimodal and Collaborative Artificial Intelligence for the Design of Inclusive and Sustainable Fashion): an immersive, human-in-the-loop analytics system designed to support fashion professionals in exploring, comparing and contextualizing product data across digital and social platforms. Unlike generative or simulation-based approaches, Made-In provides creative decision support by aggregating real-world data from luxury brand websites and social media. This enables designers and merchandisers to make informed, context-aware choices. The system comprises three core modules: a 3D configurator for visualizing product assortments; a collection grid interface for the comparative analysis of e-commerce data; and a social media trend detector based on deep learning pipelines for image classification, object detection and color clustering. Two curated datasets, one derived from Instagram and the other from fashion e-tailers, provide the system with analytics. A user study with domain experts confirms the platform’s usability and relevance for trend forecasting, sustainability evaluation and visual merchandising strategy. The results demonstrate that Made-In effectively bridges the gap between data analytics and human creativity in fashion, offering a scalable solution that aligns with EU goals for digital sustainability and inclusivity. • Immersive AI system that supports sustainable digital fashion exploration. • Real-time trend detection from Instagram enables geo-localized style insights. • Interactive 3D and collection grids enhance visual merchandising decisions. • AI modules extract product data, dominant colors, and sustainability tags. • Usability study confirms system effectiveness for designers and retailers.

Abstract In an age where consumer preferences are as diverse as they are dynamic, the ability to offer personalized fashion recommendations at scale remains a significant challenge for retailers. Consumers seek a shopping experience that not only understands their unique style preferences but also dynamically adapts to their evolving tastes. The fashion industry is at a crossroads, facing increasing consumer demand for personalization, sustainability and transparency in a rapidly evolving digital marketplace. Traditional retail practices, while rich in tradition and artistry, often struggle to up-to-date with the rapidly, ethically-conscious and technology-driven expectations of today’s consumers. “OutfitAI” is designed to address these challenges by leveraging the power of deep learning to revolutionize the fashion retail experience. By automating the process of background removal in fashion images, using advanced algorithms for personalized product matching, and integrating sustainability filters into the product discovery process, OutfitAI aims to deliver a shopping experience that is not only personalized and engaging, but also aligned with the ethical and environmental values of the contemporary consumer. Unlike existing solutions, OutfitAI uses state-of-the-art semantic segmentation for precise background removal, enabling detailed feature extraction from fashion images. This process enables accurate matching of user-uploaded images with similar fashion items from an extensive database of eco-friendly and ethically produced products sourced from leading e-tailers. Setting itself apart from the current state of the art, OutfitAI places a strong emphasis on ethical data use and privacy, implementing robust measures to ensure user privacy and transparency. It also pioneers the integration of sustainability into the digital fashion discovery process, promoting responsible consumption patterns among users. Through a comprehensive system architecture that combines technical innovation with a commitment to ethics and sustainability, OutfitAI not only addresses the technological needs of the fashion retail industry, but also responds to the growing demand for more responsible and transparent consumer technologies.

Ship detection using remote sensing and data from tracking devices like Automatic Identification System (AIS) play a critical role in maritime surveillance, supporting security, fisheries management, and efforts to combat illegal activities. However, challenges such as varying ship sizes, complex backgrounds, and intentional deactivation of AIS hinder accurate mapping. This study proposes a novel multimodal framework that integrates Sentinel-1 Synthetic Aperture Radar, Sentinel-2 and higher resolution optical imagery. It features an enhanced deep learning-based ship detection model combined with an AIS matchmaking algorithm to detect and cross-reference potentially suspicious maritime activities. The detection model is based on an enhanced You Only Look Once architecture, optimized for identifying small vessels in cluttered and noisy image backgrounds. The model achieves superior performance, surpassing state-of-the-art accuracy on multiple public datasets while reducing training time by 12% compared to baseline models. To ensure transparency within the pipeline, Eigen-CAM explainability techniques were employed, while C O 2 emissions were minimized during training using CodeCarbon , aligning the process with environmentally sustainable practices. Finally, the effectiveness of the pipeline was validated in a case study, successfully identifying potential ‘dark vessels’ and highlighting their possible involvement in suspicious activities. • Novel method tackles suspicious maritime activities using remote sensing imagery. • Multimodal framework integrates Sentinel-1 SAR, Sentinel-2, and optical imagery. • Enhanced YOLO model detects small ships in cluttered maritime environments. • Custom HS3-S2 dataset improves detection accuracy across multiple metrics. • Eigen-CAM ensures model transparency; CodeCarbon is used to report CO 2 emissions during training.

Artificial Intelligence (AI) techniques are becoming more and more sophisticated showing the potential to deeply understand and predict consumer behaviour in a way to boost the retail sector; however, retail-sensitive considerations underpinning their deployment have been poorly explored to date. This paper explores the application of AI technologies in the retail sector, focusing on their potential to enhance decision-making processes by preventing major ethical risks inherent to them, such as the propagation of bias and systems’ lack of explainability. Drawing on recent literature on AI ethics, this study proposes a methodological path for the design and the development of trustworthy, unbiased, and more explainable AI systems in the retail sector. Such framework grounds on European (EU) AI ethics principles and addresses the specific nuances of retail applications. To do this, we first examine the VRAI framework, a deep learning model used to analyse shopper interactions, people counting and re-identification, to highlight the critical need for transparency and fairness in AI operations. Second, the paper proposes actionable strategies for integrating high-level ethical guidelines into practical settings, and particularly, to mitigate biases leading to unfair outcomes in AI systems and improve their explainability. By doing so, the paper aims to show the key added value of embedding AI ethics requirements into AI practices and computer vision technology to truly promote technically and ethically robust AI in the retail domain. • Establishment of AI ethics guidelines for the retail domain. • Design of a detailed framework for AI ethics. • Evaluation of the VRAI framework on new explainability metrics. • Recommendations for AI transparency improvements. • Integration of ethics into the design of AI systems.

Shelf monitoring plays a key role in optimizing retail shelf layout, enhancing the customer shopping experience and maximizing profit margins. The process of automating shelf audit involves the detection, localization and recognition of objects on store shelves, including diverse products with varying attributes in unconstrained environments. This facilitates the assessment of planogram compliance. Accurate product localization within shelves requires the identification of specific shelf rows. To address the current technological challenges, we introduce “Shelf Management”, a deep learning-based system that is carefully tailored to redesign shelf monitoring practices. Our system can navigate the complexities of shelf monitoring by using advanced deep learning techniques and object detection and recognition models. In addition, a complex semantic module enhances the accuracy of detecting and assigning products to their designated shelf rows and locations. In particular, we recognize the lack of finely annotated datasets at the SKU level. As a contribution to the field, we provide annotations for two novel datasets: SHARD (SHelf mAnagement Row Dataset) and SHAPE (SHelf mAnagement Product dataset). These datasets not only provide valuable resources, but also serve as benchmarks for further research in the field of retail. A complete pipeline is designed using a RetinaNet architecture for object detection with 0.752 mAP, followed by a Deep Hough transform to detect shelf rows as semantic lines with an F1 score of 97%, and a product recognition step using a MobileNetV3 architecture trained with triplet loss and used as a feature extractor together with FAISS for fast image retrieval with an accuracy of 93% on top-1 recognition. Localization is achieved using a deterministic approach based on product detection and shelf row detection. Source code and datasets are available at https://github.com/rokopi-byte/shelf_management.

Abstract Public space is usually conceived as where people live, perceive, and interact with other people. The environment affects people in several different ways as well. The impact of environmental problems on humans is significant, affecting all human activities, including health and socio-economic development. Thus, there is a need to rethink how space is used. Dealing with the important needs raised by climate emergency, pandemic and digitization, the contributions of this paper consist in the creation of opportunities for developing generative approaches to space design and utilization. It is proposed GREEN PATH, an intelligent expert system for space planning. GREEN PATH uses human trajectories and deep learning methods to analyse and understand human behaviour for offering insights to layout designers. In particular, a Generative Adversarial Imitation Learning (GAIL) framework hybridised with classical reinforcement learning methods is proposed. An example of the classical reinforcement learning method used is continuous penalties, which allow us to model the shape of the trajectories and insert a bias, which is necessary for the generation, into the training. The structure of the framework and the formalisation of the problem to be solved allow for the evaluation of the results in terms of generation and prediction. The use case is a chosen retail domain that will serve as a demonstrator for optimising the layout environment and improving the shopping experience. Experiments were assessed on shoppers’ trajectories obtained from four different stores, considering two years.

Abstract In retail environments, understanding how shoppers move about in a store’s spaces and interact with products is very valuable. While the retail environment has several favourable characteristics that support computer vision, such as reasonable lighting, the large number and diversity of products sold, as well as the potential ambiguity of shoppers’ movements, mean that accurately measuring shopper behaviour is still challenging. Over the past years, machine-learning and feature-based tools for people counting as well as interactions analytic and re-identification were developed with the aim of learning shopper skills based on occlusion-free RGB-D cameras in a top-view configuration. However, after moving into the era of multimedia big data, machine-learning approaches evolved into deep learning approaches, which are a more powerful and efficient way of dealing with the complexities of human behaviour. In this paper, a novel VRAI deep learning application that uses three convolutional neural networks to count the number of people passing or stopping in the camera area, perform top-view re-identification and measure shopper–shelf interactions from a single RGB-D video flow with near real-time performances has been introduced. The framework is evaluated on the following three new datasets that are publicly available: TVHeads for people counting, HaDa for shopper–shelf interactions and TVPR2 for people re-identification. The experimental results show that the proposed methods significantly outperform all competitive state-of-the-art methods (accuracy of 99.5% on people counting, 92.6% on interaction classification and 74.5% on re-id), bringing to different and significative insights for implicit and extensive shopper behaviour analysis for marketing applications.

Preterm infants' limb-pose estimation is a crucial but challenging task, which may improve patients' care and facilitate clinicians in infant's movements monitoring. Work in the literature either provides approaches to whole-body segmentation and tracking, which, however, has poor clinical value, or retrieve a posteriori limb pose from limb segmentation, increasing computational costs and introducing inaccuracy sources. In this paper, we address the problem of limb-pose estimation under a different point of view. We proposed a 2D fully-convolutional neural network for roughly detecting limb joints and joint connections, followed by a regression convolutional neural network for accurate joint and joint-connection position estimation. Joints from the same limb are then connected with a maximum bipartite matching approach. Our analysis does not require any prior modeling of infants' body structure, neither any manual interventions. For developing and testing the proposed approach, we built a dataset of four videos (video length = 90 s) recorded with a depth sensor in a neonatal intensive care unit (NICU) during the actual clinical practice, achieving median root mean square distance [pixels] of 10.790 (right arm), 10.542 (left arm), 8.294 (right leg), 11.270 (left leg) with respect to the ground-truth limb pose. The idea of estimating limb pose directly from depth images may represent a future paradigm for addressing the problem of preterm-infants' movement monitoring and offer all possible support to clinicians in NICUs.

Detecting and tracking people is a challenging task in a persistent crowded environment (i.e. retail, airport, station, etc.) for human behaviour analysis of security purposes. This paper introduces an approach to track and detect people in cases of heavy occlusions based on CNNs for semantic segmentation using top-view depth visual data. The purpose is the design of a novel U-Net architecture, U-Net3, that has been modified compared to the previous ones at the end of each layer. In particular, a batch normalization is added after the first ReLU activation function and after each max-pooling and up-sampling functions. The approach was applied and tested on a new and public available dataset, TVHeads Dataset, consisting of depth images of people recorded from an RGB-D camera installed in top-view configuration. Our variant outperforms baseline architectures while remaining computationally efficient at inference time. Results show high accuracy, demonstrating the effectiveness and suitability of our approach.