Paper Contents
Abstract
In recent years, the integration of artificial intelligence (AI) in retail has transformed the landscape of inventory management and customer interaction. A critical aspect of this transformation is object detection, which enables automated systems to identify and locate products in retail environments. This paper presents a novel approach to domain-specific pretraining for retail object detection, addressing the challenges posed by the diverse range of products and dynamic retail settings. Traditional object detection models often struggle with the variability in product appearance, packaging, and placement, leading to decreased accuracy in real-world applications. To mitigate these issues, we propose a two-phase training methodology: initial pretraining on a large, generic dataset followed by fine-tuning on a curated dataset specifically tailored to retail scenarios. This approach leverages transfer learning to enhance model performance, ensuring that the detection system is better equipped to recognize and categorize items within the retail domain. We evaluate our method against existing benchmarks, demonstrating significant improvements in detection accuracy and processing speed. Additionally, we discuss the implications of our findings for inventory management and customer experience enhancement in retail settings. Our research highlights the importance of domain-specific knowledge in the training of object detection models and paves the way for future advancements in AI applications for retail, ultimately contributing to a more efficient and responsive shopping environment.
Copyright
Copyright © 2023 Murali Mohana Krishna Dandu, Dasaiah Pakanati, Harshita Cherukuri, Om Goel, Dr. Shakeb Khan, Er. Aman Shrivastav. This is an open access article distributed under the Creative Commons Attribution License.
