Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал:
https://repository.hneu.edu.ua/handle/123456789/37349
Назва: | Diagnostics of children's emotional state based on intellectual multimodal analysis of drawings |
Автори: | Korablyov M. Dykyi S. Fomichov O. Kobzev I. |
Теми: | children's drawings emotional state diagnostics neural network multimodal model EfficientNet-B3 YOLOv8 attention f |
Дата публікації: | 2025 |
Бібліографічний опис: | Korablyov M. Diagnostics of children's emotional state based on intellectual multimodal analysis of drawings / M. Korablyov, S. Dykyi, O. Fomichov and other // ICST-2025: Information Control Systems & Technologies, September 24-26, 2025. - Odesa, 2025. - Pp. 42-54. |
Короткий огляд (реферат): | The emotional state of a child is a complex, multidimensional construct, reflected in the choice of color, composition, symbolic images, and strokes in the drawing, which is formed through a non-linear, chaotic creative process. Traditional psychological analysis of children's drawings relies on subjective interpretation and is not scalable for mass screening. This paper proposes a neural network multimodal hybrid model for automated emotion diagnostics, combining four complementary feature channels. The pre-trained EfficientNet-B3 neural network extracts the global context of the image; the YOLOv8 neural network determines local semantically significant objects, expanded to 55 classes on the open ESRA dataset; the color palette is described by the statistics of the HSV (Hue, Saturation, Value) space; compositional and graphic metrics encode the geometry and character of the lines. For adaptive weighting of channel contributions, a lightweight attention-fusion layer is introduced, forming a 256-dimensional combined feature vector. The final classifier based on a multilayer perceptron (MLP) matches a drawing to one of three emotional categories - "Happiness", "Anxiety/Depression", "Anger/Aggression", achieving an accuracy of 80-85% on a combined test set from Kaggle. A key benefit is the interpretable JSON report, which contains class probabilities and numerical indicators of color, composition, and detected objects. This makes the results easier to use in practice by a psychologist and increases confidence in the model. |
URI (Уніфікований ідентифікатор ресурсу): | https://repository.hneu.edu.ua/handle/123456789/37349 |
Розташовується у зібраннях: | Статті (МСТ) |
Файли цього матеріалу:
Файл | Опис | Розмір | Формат | |
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Diagnostics of Children's Emotional State Based.pdf | 775,06 kB | Adobe PDF | Переглянути/відкрити |
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