<p>In the task of fabric defect detection, there are problems such as missed detection and false detection caused by defects with large aspect ratios and the presence of complex fabric backgrounds. We propose an improved fabric defect detection method based on YOLOv7 (You Only Look Once version 7), which aims to reduce network parameters while increasing the detection accuracy. Firstly, a double-branch partial convolution module (DBPM) is incorporated into the backbone network to reduce the amount of the network parameters while improving detection accuracy. Secondly, the simple attention mechanism (SimAM) is introduced in the backbone network to enhance the feature extraction ability of defects of various sizes and shapes without introducing additional parameters. Finally, the neck network is reconstructed into a lighter feature fusion network (LFFNet) to further reduce the number of network parameters. By testing the datasets, it is evident that compared to the original algorithm, the improved algorithm reduces the floating point operations (FLOPs) of the improved algorithm is reduced by 51.1%, the parameters amount decreases by 36.3%, the mean average precision (mAP@0.5) is increased by 5.1%, and the missed detection rate is reduced by 3.6%.</p>