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Detail
Conference Paper
Gradual Semi-Automatic Annotation and Hybrid Model for Effective Detection of Garbage Bags
- Authors
- Hoseok Hwang, Aekyeung Moon, Seungwoo Son
- Issue Date
- 2023-12
- Citation
- International Conference on Information Technology (ICIT) 2023, pp.25-30
- Language
- English
- Type
- Conference Paper
- Abstract
- As the amount of unauthorized garbage dumping keeps rising, the existing labor-intensive approach for monitoring dumping actions unleashes the study for automatic detection systems. However, since previous studies focused on explicit dumping behavior, they have difficulty applicable to real-world situations. To find implicit dumping behavior, we propose combining object detection and color classification models to detect unauthorized garbage bags. Our approach employs a gradual semi-automatic annotation method inspired by semi-supervised learning to create a model. After using gradual semi-automatic annotation to extract training data, we train a suitable model for the CCTV datasets. We detect only garbage bags attached to people using IoU (intersection-over-union) distance to reduce false detection. To evaluate the effectiveness of the proposed method, we measured the model’s accuracy and compared it with the existing model. The experimental evaluation demonstrates that the proposed annotation method shows 92.8% model accuracy and 90.9% annotation accuracy. In addition, the classification model using data growth and reduction confirms that the F1-score is increased by 6.3%.
- KSP Keywords
- Automatic Detection, Classification models, Detection Systems(IDS), F1-score, False detection, Hybrid model, Model accuracy, Real-world, Semi-Supervised Learning(SSL), Semi-automatic annotation, color classification
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
