The textile industry’s pursuit of defect-free production has driven the demand for efficient automated inspection systems, yet low-resolution imaging and subtle defect visibility remain critical challenges. Traditional manual inspection is limited by high labor costs, low repeatability, and subjective judgments, while high-resolution camera systems impose significant hardware constraints. This study proposes a cost-efficient pipeline that integrates lightweight super-resolution (SR) enhancement and real-time defect detection to narrow the performance gap with native high-resolution imaging. The SR module, derived from ESRGAN, is optimized via two complementary modifications: reducing block depth and channel width in residual-in-residual dense blocks (RRDB) and replacing standard convolutions with depthwise-separable convolutions (SRRDB), achieving a 24–44% reduction in per-patch latency and a 4–5× decrease in model size. Enhanced images are partitioned into 4×4 non-overlapping tiles, enabling precise defect localization and segmentation via single-stage detectors. Here we show that the pipeline delivers real-time throughput of 28.4 FPS on a high-performance computing (HPC) environment, with YOLOv8 variants achieving balanced accuracy—particularly for hole, oil stain, and object defects—with mAP50 values exceeding 0.84 on the TILDA dataset. For edge deployment on Raspberry Pi 4B/5, the lightweight SRRDB configuration reduces SR latency by 29% compared to baseline RRDB. This work demonstrates that low-cost cameras, augmented by optimized SR and tiled detection, can provide a practical alternative to high-resolution imaging systems, advancing industrial quality control in textile production while balancing computational efficiency and detection performance. The relevant code is available at https://github.com/ahmet-metin/textile-defect-sr-pipeline.