Defect detection in striped fabrics has always been an important task for fabric production companies. Due to the interference caused by different background stripe color combinations and minimal period variations, visual small-sized detection of defects on fabrics with varying background textures and stripes becomes a challenging engineering problem. To address the issue of missing small-sized targets in striped fabric defect detection tasks, this study investigates a multi-scale detection method for striped fabric defects enhanced by coordinate attention. A multi-scale detection module with small-scale detectors was designed to enabling the detection model to identify small-sized defect features. Additionally, a coordinate attention-based defect feature extraction module was developed, which integrates coordinate information in both vertical and horizontal directions of the feature matrix to learn the spatial coordinate relationships of elongated defects, rigorous experiments have shown that using this method can improve precision by 0.47% and recall by 9.17%, respectively, compared to baseline methods. This effectively enhances the detection rate of small-sized and elongated defects, improving the model's performance on striped fabrics.