Work

FF-YOLO: Fashion Fabric Detection Algorithm Based on YOLOv8

Year: 2024

Type: article

Abstract: In monitoring fashion trend and managing inventory, promptly detecting and identifying popular fashion fabric material is crucial for manufacturers and sellers. However, most traditional fabric detection are working on raw material(e.g. a block of raw fabric material with a solid background), which is not robust enough to handle complex fashion images(e.g. a human model and complex backgrounds). To address this problem, this paper proposes a lightweight and efficient fashion fabric detection algorithm named FF-YOLO, based on the YOLOv8 architecture. First, we introduce a Simple Attetion Module (SimAM) in the backbone to improve the feature extraction capability of the model. Furthermore, we propose a Lightweight Multi-Level Asymmetry Detector Head (LADH) to replace the head, improving the computational efficiency of the model’s inference process. Last, we replace the original loss function with Wise-IoU to improve the localization ability of the detection box. The experimental results show that FF-YOLO achives an average accuracy of 75.5% and a frames per second (FPS) of 105 for fashion fabric detection. Compared to the original YOLOv8 model, the mAP is improved by 2.9% and the FPS is improved by 6 frames. Meanwhile, the floating point operations per second (FLOPS) computational complexity is reduced by 5%. The results show our method proves to be effective and lightweight in fashion fabric detection tasks.

Source: IEEE Access

DOI: 10.1109/access.2024.3524618

Author: Caixia Chen

Institution: Donghua University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,995

Funders: Shanghai University, Fundamental Research Funds for the Central Universities