Running on marked tracks is central to athletic training, still athletes with visual impairment depend on external guides limiting independence in training and competition. Advancements in assistive and navigation technologies offer new possibilities for inclusive sports environments. Therefore, there is an urgent need to develop inclusive mobility solutions that empower visually impaired athletes. In this work, we focus on an autonomous lane guidance system designed to assist athletes in independent navigation. To address this investigation, we propose an end-to-end vision-based system built upon our TrackAid Dataset (TrackAid-DT). The system operates under real-world conditions and supports safe athletic running by delivering vibrotactile haptic feedback for real-time guidance, in contrast to prior works that primarily address walking or jogging. TrackAid-DT comprises egocentric, pixel-annotated monocular images acquired from chest-mounted cameras under diverse orientations, speeds, and lighting conditions, and is designed to train segmentation models that enable the proposed guidance system and its deployment on edge devices. We benchmark multiple advanced segmentation models using eight-fold cross validation, task specific pretraining with public KITTI dataset and report segmentation accuracy together with computational efficiency on both training and generalization sets. Among the considered models, pretrained TransUNet achieved the strongest segmentation quality (IoU 0.929 ± 0.064, F1 0.962 ± 0.036), while U-Net offered a more deployable balance (IoU 0.921 ± 0.064, 80.31 ms/frame, 12.45 FPS, and the lowest environmental impact at 16.49 g CO 2 e). We consider the trade-offs and deploy the system on multiple edge devices and validate it through outdoor prototype testing showing its practical effectiveness in real-world running scenarios. The system maintains stable lane detection and delivers timely haptic feedback at speeds of up to 10 km/h, demonstrating feasibility for autonomous vision based guidance in athletic running. To foster further development in this domain all code and data are publicly released (link: https://github.com/vrai-group/TrackAid-DT ).

Patent databases represent a large-scale, heterogeneous form of Big Data that captures technological evolution, diffusion, and commercialization potential of technologies. Traditional systematic mapping approaches such as the Technology Function Matrix (TFM) are widely used to correlate technical solutions with functional needs of society. However, the nature of the data that is utilized in the mapping process lacks indicators of market adoption which portrays the ground level potential of technological innovation. Therefore, such mappings do not fairly represent the technologies that have been widely adopted and cloud the judgement of stakeholders. Addressing this, we propose an Adoption-weighted Technology Function Matrix (ATFM), an analytics framework that integrates functional mapping with adoption-oriented indicators such as forward citations, grant rate, family size, and claims. Using a large dataset of 15,807 patents grants (2015–2025), we systematically showcase the procedure to apply ATFM to a case study of China's smart elderly care sector. The framework reveals not only technology-function linkages but also differential adoption patterns across domains. Our results highlight adoption hotspots in IoT-based sensing, low-latency 5G communication, and AI-driven health management, while identifying gaps in privacy protection and predictive diagnostics. Compared to conventional TFM, ATFM provides a more grounded, scalable and data-driven methodology that enriches patent analytics with adoption insights, offering value for both academic research in large scale dataset and practical decision-making in emerging industries and government stakeholders.

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 prevalence of crimes involving handguns and knives underscores the importance of early weapon detection. This, along with the spread of video surveillance systems, boosted the development of automatic approaches for weapon detection from surveillance cameras. Despite the advancements from classical computer vision to Deep Learning (DL) techniques, accurately detecting weapons in real-time remains challenging due to their small size. Current DL methods, which attempt to mitigate this issue using complex detection architectures, are resource-intensive, resulting in high costs and energy usage, and hindering their deployment on efficient edge devices. This creates challenges in resource-limited environments, making these methods impractical for edge and real-time applications. To address these shortcomings, our work proposes YOLOSR, which integrates a You Only Look Once (YOLO) v8-small model with an Enhanced Deep Super Resolution (EDSR)-based network using a shared backbone. During training, the auxiliary Super Resolution (SR) helps in learning better features, which could benefit the weapon detection task. During inference, the SR branch is removed, keeping the detector’s computational complexity unchanged. The YOLOSR’s accuracy and efficiency were validated on our WeaponSense dataset and on a NVIDIA Jetson Nano, against other weapon detectors. The results exhibited that YOLOSR, compared to the state-of-the-art YOLOv8-small model, maintained the same computational complexity with 28.8 billion floating point operations and on-device latency of 101 ms per image, while increasing the Average Precision by 10.2 percentage points. Thus, the YOLOSR emerges as an effective solution for real-time weapon detection in resource-constrained environments, achieving an optimal trade-off between efficiency and accuracy. • YOLOSR merges Super-Resolution (SR) with YOLOv8 to enhance small weapon detection. • SR boosts training and is discarded at inference, with no extra computational cost. • YOLOSR achieved a 10.2% AP gain compared to YOLOv8, keeping the same real-time speed. • The approach runs at 101ms/image on Jetson Nano, enabling real-time edge inference. • The approach was validated on WeaponSense, a realistic video-surveillance dataset.

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.

Abstract The early detection of handguns and knives from surveillance videos is crucial to enhance people’s safety. Despite the increasing development of Deep Learning (DL) methods for general object detection, weapon detection from surveillance videos still presents open challenges. Among these, the most significant are: (i) the very small size of the weapons with respect to the camera field of view and (ii) the need of a real-time feedback, even when using low-cost edge devices for computation. Complex and recently-developed DL architectures could mitigate the former challenge but do not satisfy the latter one. To tackle such limitation, the proposed work addresses the weapon-detection task from an edge perspective. A double-step DL approach was developed and evaluated against other state-of-the-art methods on a custom indoor surveillance dataset. The approach is based on a first Convolutional Neural Network (CNN) for people detection which guides a second CNN to identify handguns and knives. To evaluate the performance in a real-world indoor environment, the approach was deployed on a NVIDIA Jetson Nano edge device which was connected to an IP camera. The system achieved near real-time performance without relying on expensive hardware. The results in terms of both COCO Average Precision (AP = 79.30) and Frames per Second (FPS = 5.10) on the low-power NVIDIA Jetson Nano pointed out the goodness of the proposed approach compared with the others, encouraging the spread of automated video surveillance systems affordable to everyone.

Nowadays the inspection of products at the end of line represents a critical phase. At this stage, it is necessary to look for defects in order to prevent the quality check to fail, and to provide information for improving the production as well. This task can be performed by using several sensing technologies, and the contactless optical inspection plays a key role. In this regard, the use of advanced robotic manipulators offers the capability to change the viewpoint of a given object and to inspect its multiple faces. We propose an approach that combines the use of photometric stereo to derive a 3D model of objects, empowered by the super-resolution that is applied on the original dataset (upstream) or on the normal images (downstream) in order to increase the quality of the final 3D model. The vision system is mounted on a robotic manipulator, able to grasp and change the viewpoint, thus offering a more complete view of the object to be inspected. The obtained results show that the developed solution increases the quality of the derived 3D models used for inspection tasks on different faces of the objects; this is achieved by using the manipulation ability offered by the adopted robotic platform.