Search Results (77)

This paper explores the potential of public webcams as a source of data for transport research. Eight different open-source object detection models were tested on three publicly accessible webcams located in the city of Brunswick, Germany. Fifteen images at different lighting conditions (bright light, dusk, and night) were selected from each webcam and manually labelled with regard to the following six categories: cars, persons, bicycles, trucks, trams, and buses. The manual counts in these six categories were then compared to the number of counts found by the object detection models. The results show that public webcams constitute a useful source of data for transport research. In bright light conditions, applying out-of-the-box object detection models can yield reliable counts of cars or persons in public squares, streets, and junctions. However, the detection of cars and persons was not reliably accurate at dusk or night. Thus, different object detection models might have to be used to generate accurate counts in different lighting conditions. Furthermore, the object detection models worked less well for identifying trams, buses, bicycles, and trucks. Hence fine-tuning and adapting the models to the specific webcams might be needed to achieve satisfactory results for these four types of traffic participants. Full article

As a forward-looking development in air traffic control (ATC), remote towers rely on virtualized information presentation, which may exacerbate visual fatigue among controllers and compromise operational safety. This study proposes a visual fatigue recognition model based on multimodal physiological signals. A 60-min simulated remote tower task was conducted with 36 participants, during which eye-tracking (ET), electroencephalography (EEG), electrocardiography (ECG), and electrodermal activity (EDA) signals were collected. Subjective fatigue questionnaires and objective ophthalmic measurements were also recorded before and after the task. Statistically significant features were identified through paired t-tests, and fatigue labels were constructed by combining subjective and objective indicators. LightGBM was then employed to rank feature importance by integrating split frequency and information gain into a composite score. The top 12 features were selected and used to train a multilayer perceptron (MLP) for classification. The model achieved an average balanced accuracy of 0.92 and an F1 score of 0.90 under 12-fold cross-validation, demonstrating excellent predictive performance. The high-ranking features spanned four modalities, revealing typical physiological patterns of visual fatigue across ocular behavior, cortical activity, autonomic regulation, and arousal level. These findings validate the effectiveness of multimodal fusion in modeling visual fatigue and provide theoretical and technical support for human factor monitoring and risk mitigation in remote tower environments. Full article

Excessive sugar consumption has been reported to be associated with various health issues such as obesity, diabetes, cardiovascular diseases, and dental problems. In the UK, effective strategies have been implemented to reduce sugar intake, including the Change4Life Sugar Smart campaign, product reformulation, traffic light labelling, portion control, and the Soft Drinks Industry Levy (SDIL). This review of empirical studies (n = 11) shows that product reformulation, especially in beverages and packaged foods, is effective, as consumers can prefer reduced-sugar alternatives when clearly labelled. The UK traffic light labelling scheme and portion control were also reported to help consumers make informed, healthier food choices. The SDIL, introduced in 2018, was also found to significantly lower sugary beverage consumption. While progress is evident, further nutrition education, public awareness, particularly for people with low socioeconomic status, and more comprehensive policies for long-term positive dietary behavioural shift are essential to limit diseases and conditions associated with high sugar consumption. Future research must evaluate the combined effects of these interventions and examine their long-term effectiveness across diverse population groups. Full article

Roadside camera systems are commonly used for traffic data collection, yet conventional optical systems are limited by poor performance in varying weather and light conditions and are often restricted by data privacy regulations. Thermal imaging overcomes these issues, enabling reliable detection across all conditions without collecting personal data. However, its widespread use is hindered by the scarcity of diverse, annotated thermal training data, especially since fixed cameras installed at the side of the road produce very similar images with the same backgrounds. This paper presents two key innovations to address these challenges: a novel dataset of 11,400 annotated images and 142 unannotated video clips, the largest and most diverse available for thermal roadside imaging to date, and a weakly supervised incremental learning framework tailored for thermal roadside imagery. The dataset supports the development of self-supervised algorithms, and the learning framework allows efficient adaptation to new camera viewpoints and diverse environmental conditions without additional labelling. Together, these contributions enable cost-effective and reliable thermal-based traffic monitoring across varied locations, achieving an 8.9-point increase in mean average precision for previously unseen viewpoints. Full article

Traffic safety remains a pressing global concern, with traffic signs playing a vital role in regulating and guiding drivers. However, environmental factors like lighting and weather often compromise their visibility, impacting human drivers and autonomous vehicle (AV) systems. This study addresses critical traffic sign detection (TSD) and classification (TSC) gaps by leveraging the YOLOv8 algorithm to evaluate the detection accuracy and sign quality under diverse lighting conditions. The model achieved robust performance metrics across day and night scenarios using the novel ZND dataset, comprising 16,500 labeled images sourced from the GTSRB, GitHub repositories, and real-world own photographs. Complementary retroreflectivity assessments using handheld retroreflectometers revealed correlations between the material properties of the signs and their detection performance, emphasizing the importance of the retroreflective quality, especially under night-time conditions. Additionally, video analysis highlighted the influence of sharpness, brightness, and contrast on detection rates. Human evaluations further provided insights into subjective perceptions of visibility and their relationship with algorithmic detection, underscoring areas for potential improvement. The findings emphasize the need for using various assessment methods, advanced algorithms, enhanced sign materials, and regular maintenance to improve detection reliability and road safety. This research bridges the theoretical and practical aspects of TSD, offering recommendations that could advance AV systems and inform future traffic sign design and evaluation standards. Full article

The ability to accurately detect traffic light color is critical for the functioning of Advanced Driver Assistance Systems (ADAS), as it directly impacts a vehicle’s safety and operational efficiency. This paper introduces Meta-YOLOv8, an improvement over YOLOv8 based on meta-learning, designed explicitly for traffic light color detection focusing on color recognition. In contrast to conventional models, Meta-YOLOv8 focuses on the illuminated portion of traffic signals, enhancing accuracy and extending the detection range in challenging conditions. Furthermore, this approach reduces the computational load by filtering out irrelevant data. An innovative labeling technique has been implemented to address real-time weather-related detection issues, although other bright objects may occasionally confound it. Our model employs meta-learning principles to mitigate confusion and boost confidence in detections. Leveraging task similarity and prior knowledge enhances detection performance across diverse lighting and weather conditions. Meta-learning also reduces the necessity for extensive datasets while maintaining consistent performance and adaptability to novel categories. The optimized feature weighting for precise color differentiation, coupled with reduced latency and computational demands, enables a faster response from the driver and reduces the risk of accidents. This represents a significant advancement for resource-constrained ADAS. A comparative assessment of Meta-YOLOv8 with traditional models, including SSD, Faster R-CNN, and Detection Transformers (DETR), reveals that it outperforms these models, achieving an F1 score, accuracy of 93% and a precision rate of 97%. Full article

Analyzing IoT data is an important challenge in the smart cities domain due to the complexity of network traffic generated by a large number of interconnected devices: smart cameras, light bulbs, motion sensors, voice assistants, and so on. To overcome this issue, a multi-agent system is proposed to deal with all machine learning steps, from preprocessing and labeling data to discovering the most suitable model for the analyzed dataset. This paper shows that dividing the work into different tasks, managed by specialized agents, and evaluating the discovered models by an Expert System Agent leads to better results in the learning process. Full article

In response to growing public health concerns, governments worldwide have implemented various nutrition labelling schemes to promote healthier eating habits. This study aimed to assess the consistency and effectiveness of these labels in an out-of-home context, specifically focusing on restaurant, hospitality, and institutional food service settings. In total, 178 different dishes from Spain were analysed using labels from the Mazocco method, the UK’s traffic light system, the Health Star Rating (Australia), Nutri-Score (France), multiple traffic lights (Ecuador), and warning labels (Chile and Uruguay). The results demonstrated a generally low level of agreement among these labels (K < 0.40), indicating notable variability and a lack of consensus, which could hinder consumers’ ability to make informed food choices in out-of-home settings. Nutri-Score classified the highest number of dishes as unhealthy (38%). This study underscores the need for an easy-to-understand labelling system tailored to each country’s culinary and socio-cultural contexts to improve consumer decision-making in various dining environments. Future research should focus on developing and testing qualitative methods to more accurately gauge the nutritional quality of cooked dishes in diverse out-of-home settings, thereby enhancing public health outcomes. By addressing the specific needs of the home, restaurants, hospitality, and institutional food services, tailored labelling schemes could significantly improve consumers’ ability to make healthier food choices. Full article

The prevalence of overweight and obesity among Chinese residents has become a pressing public health concern. The UK Multiple Traffic Light labeling system, known for its user-friendly design, has demonstrated success in promoting healthier food choices. This paper presents novel findings from a randomized controlled experiment assessing the impact of traffic light labeling on Chinese consumers’ food choices. Results indicate that the label significantly reduces the intake of calories, fat, carbohydrates, and sodium without increasing the economic costs of food choices. This study contributes empirical evidence to the effectiveness of traffic light labeling in China, with implications for the country’s approach to front-of-pack nutrition labeling. Full article

Background: This study investigates the efficacy of Front-of-Pack Nutrition Labels (FOPNLs) as a cost-effective tool for improving dietary choices among Greek consumers. The purpose of the study was to investigate Greek customers’ preferences and comprehension of commonly used European FOPNL schemes. Methods: The Hellenic Food Authority and the Agricultural University of Athens performed a representative online survey in March 2022, titled “The Role of Nutritional Labelling in Public Perception and Food Procurement.” Consumers responded to a questionnaire separated into two parts. Part one included (i) personal, sociodemographic information, and (ii) subjective opinions on the FOPNL schemes, and part two comprised (iii) an objective understanding of NutriScore and NutrInform Battery, using 15 different foods. Participants were randomly allocated to these groups, and general mixed models were used for analysis. Results: A total of 1389 adults completed the first part of the survey, and 74.8% completed the second part. The Multiple Traffic Lights scheme was the preferred FOPNL, chosen by 48.4% of respondents, compared to 19.7% for NutrInform Battery and 12.3% for NutriScore. However, the mean objective assessment score was highest for NutriScore (5.8 ± 2.3) compared to NutrInform Battery (5.4 ± 1.9). Conclusion: The results highlight the necessity for comprehensive nutrition education programs by showing a considerable gap between subjective preferences and an objective understanding of nutrition labels. Full article

Advances in deep learning and computer vision have overcome many challenges inherent in the field of autonomous intelligent vehicles. To improve the detection accuracy and efficiency of EdgeBoard intelligent vehicles, we proposed an optimized design of EdgeBoard based on our PP-YOLOE+ model. This model innovatively introduces a composite backbone network, incorporating deep residual networks, feature pyramid networks, and RepResBlock structures to enrich environmental perception capabilities through the advanced analysis of sensor data. The incorporation of an efficient task-aligned head (ET-head) in the PP-YOLOE+ framework marks a pivotal innovation for precise interpretation of sensor information, addressing the interplay between classification and localization tasks with high effectiveness. Subsequent refinement of target regions by detection head units significantly sharpens the system’s ability to navigate and adapt to diverse driving scenarios. Our innovative hardware design, featuring a custom-designed mainboard and drive board, is specifically tailored to enhance the computational speed and data processing capabilities of intelligent vehicles. Furthermore, the optimization of our Pos-PID control algorithm allows the system to dynamically adjust to complex driving scenarios, significantly enhancing vehicle safety and reliability. Besides, our methodology leverages the latest technologies in edge computing and dynamic label assignment, enhancing intelligent vehicles’ operations through seamless sensor integration. Our custom dataset, specifically designed for this study, includes 4777 images captured by intelligent vehicles under a variety of environmental and lighting conditions. The dataset features diverse scenarios and objects pertinent to autonomous driving, such as pedestrian crossings and traffic signs, ensuring a comprehensive evaluation of the model’s performance. We conducted extensive testing of our model on this dataset to thoroughly assess sensor performance. Evaluated against metrics including accuracy, error rate, precision, recall, mean average precision (mAP), and F1-score, our findings reveal that the model achieves a remarkable accuracy rate of 99.113%, an mAP of 54.9%, and a real-time detection frame rate of 192 FPS, all within a compact parameter footprint of just 81 MB. These results demonstrate the superior capability of our PP-YOLOE+ model to integrate sensor data, achieving an optimal balance between detection accuracy and computational speed compared with existing algorithms. Full article

Food composition data in the Eastern Mediterranean Region countries are often lacking, obsolete, or unreliable. The study aims to provide reliable nutrient data on food products consumed in Oman in order to evaluate their nutritional quality, the consistency of the nutrition labeling and claims, and, ultimately, the use for food consumption surveys and update the current food composition database. Contents of fat, fatty acids, carbohydrates, protein, sugars, and sodium were chemically analyzed in 221 foods and beverages. Products were classified according to their nutritional composition and the extent of processing and coded according to the FoodEx2 system. Labels and laboratory values were compared using the tolerance levels of the European Union. Results indicate that the nutrition labeling aligns with the values obtained in the laboratory, with the exception of 6.3% discrepancies in TFA content, where the reported values are higher than the appropriate reference values. The most frequent category (71.5%) was ultra-processed foods. In terms of inconsistencies in the nutritional claims, 5.1% of food products with claims did not comply with the statement “sugar-free” or “low salt”. Our study provides evidence to support the necessity of comprehensive recommendations for consumers and food industries, which are aimed at enhancing the nutritional quality of products and augmenting consumer awareness. Full article

Sustainability labelling on food products can help consumers make informed purchasing decisions and support the urgent transition to sustainable food systems. While there is a relatively robust body of evidence on health and nutrition labelling, less is known about the effectiveness of sustainability labelling in facilitating sustainable food choices. This paper investigates the impact of sustainability labelling on consumer understanding, attitudes, and behaviour to support a more nuanced, detailed, and holistic understanding of the evidence. Using a narrative literature review methodology, the paper assesses studies covering environmental, social, and/or animal welfare aspects of sustainability labelling on food products. We found that consumer understanding of sustainability information is often limited, which could hinder behaviour change. While sustainability labelling can influence consumer attitudes and purchasing behaviours, evidence from real consumer settings tends to show small effect sizes. Consumers are generally willing to pay more for sustainability-labelled products, and organic labelling often leads to the highest reported willingness to pay. The review emphasises the importance of trust, suggesting a preference for labelling backed by governments or public authorities. Sustainability labelling that uses intuitively understandable cues has an increased impact, with visual aids such as traffic light colours showing promise. We conclude that further research is needed in real-world settings, using representative populations and exploring the influence of demographic factors, values, and attitudes. Full article

This study aims to compare the classification of foods available in the Portuguese market using Nutri-Score and NOVA classifications and to analyse their ability to discriminate the fat, saturated fat, sugar, and salt content of foods. A sample of 2682 food products was collected. The nutritional quality of foods was established using the Nutri-Score, classifying them into five categories (from A to E). The NOVA classification was used to classify foods according to the degree of food processing into unprocessed/minimally processed foods, processed culinary ingredients, processed foods, and ultra-processed foods (UPF). The nutritional content of food products was classified using a Multiple Traffic Light label system. It was observed that 73.7% of UPF were classified as Nutri-Score C, D, and E, 10.1% as Nutri-Score A, and 16.2% as Nutri-Score B. Nutri-Score was positively correlated with NOVA classification (ρ = 0.140, p < 0.001) and with the Multiple Traffic Lights system (ρ_{Total Fat} = 0.572, ρ_{Saturated Fat} = 0.668, ρ_Sugar = 0.215, ρ_Salt = 0.321, p < 0.001). NOVA classification negatively correlated with the Multiple Traffic Lights system for total fat (ρ = −0.064, p < 0.001). Our findings indicate the presence of many UPFs in all Nutri-Score categories. Since food processing and nutritional quality are complementary, both should be considered in labelling. Full article

Social relationships in the digital sphere are becoming more usual and frequent, and they constitute a very important aspect for all of us. Violent interactions in this sphere are very frequent, and have serious effects on the victims. Within this global scenario, there is one kind of digital violence that is becoming really worrying: sexism against women. Sexist comments that are publicly posted in social media (newspaper comments, social networks, etc.), usually obtain a lot of attention and become viral, with consequent damage to the persons involved. In this paper, we introduce an anti-sexism alert system, based on natural language processing (NLP) and artificial intelligence (AI), that analyzes any public post, and decides if it could be considered a sexist comment or not. Additionally, this system also works on analyzing all the public comments linked to any multimedia content (piece of news, video, tweet, etc.) and decides, using a color-based system similar to traffic lights, if there is sexism in the global set of posts. We have created a labeled data set in Spanish, since the majority of studies focus on English, to train our system, which offers a very good performance after the validation experiments. Full article