![]() In Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments (pp. ![]() Robotic maintenance of road infrastructures: The HERON project. HERON Summary Paper: Katsamenis, I., Bimpas, M., Protopapadakis, E., Zafeiropoulos, C., Kalogeras, D., Doulamis, A., Doulamis, N., Martín-Portugués Montoliu, C., Handanos, Y., Schmidt, F., Ott, L., Cantero, M., & Lopez, R.TraCon: A novel dataset for real-time traffic cones detection using deep learning. E., Davradou, A., Protopapadakis, E., Doulamis, A., Doulamis, N., & Kalogeras, D. TraCon Summary Paper: Katsamenis, I., Karolou, E.If you use or find the TraCon dataset useful, please cite the following: This work has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 955356 (Improved Robotic Platform to perform Maintenance and Upgrading Roadworks: The HERON Approach). Among the training data, 80% of them were used for training and the remaining 20% for validation. From the images of the whole dataset, 92.5% were used for training the deep model, and 7.5% for testing its effectiveness. Representative samples of the dataset are demonstrated in the figure below. It is underlined that the total number of traffic cones in the entire dataset is 947. The total number of RGB images in the dataset is 540 with various resolutions ranging from 114×170 to 2,100×1,400. txt file with the bounding box information of the traffic cones (YOLO annotation format: object-class, center_x, center_y, width, height). Because the white color is a good background for displaying logos, they often get used for branding in valet parking. The white color stands out against the background so they are handy for designating the location of entrances - restrooms and other types of entry ways. In parallel, several images include various types of occlusions, thus making the traffic cone detection task more challenging. White traffic cones are used to mark safe places. Furthermore, the images vary in terms of illumination conditions (e.g., overexposure, underexposure), environmental landscapes (e.g., highways, bridges, cities, countrysides), and weather conditions (e.g., cold, hot, sunny, windy, cloudy, rainy, and snowy). The dataset contains RGB data from heterogeneous sources and sensors (e.g., DSLR cameras, smartphones, UAVs). TraCon: A novel dataset for real-time traffic cones detection using deep learning Dataset Description
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