Automation of industrial objects counting, using Few-Shot Counting and Feature Detection approach.
Counting industrial objects is challenging due to their similar appearances and complex shapes. This paper adapts Few-Shot Counting (FSC) to minimize labeled data requirements while improving accuracy. We use Fam- Net with rule-based feature detection to enhance robustness in industrial settings. Additionally, we introduce the INDT dataset, focusing on diverse industrial objects. Our approach integrates density map estimation with feature detection to improve interpretability and reduce over-counting errors. Experimental results show improved accu- racy on industrial objects and strong generalization to other datasets, highlighting FSC’s potential for industrial automation, with future work aimed at optimizing model structure and feature extraction for further performance improvements.
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Our datasets can be found at Download Datasets
- Python 3.10.11
- pip 25.0.1
- How to run the program
- Step-by-step bullets
pip install -r requirements.txt
Piyachet Pongsantichai LinkedIn
- 0.1
- Initial Release
This project is licensed under the MIT License - see the .md file for details