Correction to: A novel finetuned YOLOv6 transfer learning model for real‑time object detection

Gupta, Chhaya; Gill, Nasib Singh; Gulia, Preeti; Chatterjee, Jyotir Moy

doi:10.1007/s11554-023-01313-8

Cited by 3 publications

(1 citation statement)

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“…Previous research on attributes recognition typically used image analysis or object detection algorithms. The extraction and expression capabilities of methods for extracting low-level features of images (such as Sift [6], HOG [7], DPM [8] are limited; Object detection algorithms (such as Faster-RCNN [9], SSD [10], YOLO series [11] still have problems such as low detection e ciency and large model parameters, making it di cult to popularize in practical scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Fine-grained Attributes Recognition Model for Clothing Based on Improved the CSPDarknet and PAFPN Network

Pan,

Xiang,

Zhang

et al. 2024

Preprint

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An efficient and accurate recognition model for fine-grained attributes of clothing has significant business prospects and social implications. However, the inherent diversity and complexity of clothing makes acquiring datasets with fine-grained attributes a costly endeavor. To address these challenges, we propose a lightweight clothing fine-grained attributes recognition model. First, the Ghost module is introduced into the CSPDarknet network to enhance the depth and expressiveness of feature learning while reducing the parameters and computational complexity. Then, the Conv module is replaced with the GSConv module in the PAFPN network to further reduce the network computational load, and the SE attention mechanism is also added to enhance the perception of key features. Finally, the Detect module is utilized to achieve effective recognition of fine-grained attributes of clothing. To evaluate the model performance, we construct a clothing dataset containing 20 fine-grained attributes. The experimental results show that the model achieves precision, recall and mAP of 76.2%, 78.9% and 81.7%. Compared to the original model, the parameters are reduced by 26.2%, and the FPS is improved by 25.4%. Our proposed model performs well on the small-scale dataset and improves its performance in resource-constrained environments, which has practical applications in clothing recommendation, virtual fitting, and personalization.

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