| Abstrakt: |
An improved algorithm based on YOLOv8, YOLOv8-MBRGA, is proposed for the task of detecting garment defects. The BiFPN pyramid is introduced to replace the concatenation in the head layer to transfer semantic information to different feature scales, thus enhancing feature fusion. To accelerate the convergence speed and inference speed of the model, RepVGG network is added to the detection head, which helps to better train the deep network model. Separate convolution is used to replace Conv convolution to reduce the complexity of the network and incorporate the attention mechanism EffectiveSE to enhance the feature extraction and multi-scale information fusion of the model. The experimental results show that the YOLOv8-MBRGA algorithm obtains significant results in garment blemish detection, with an increase of 5.5% in the mean average accuracy, an increase of 11.06% in precision, and a reduction of 30.48% in the computational effort of the model, while the inference speed remains essentially unchanged. [ABSTRACT FROM AUTHOR] |
