In the process of fabric production, various types of defects affect the quality of a fabric. However, due to the wide variety of fabric defects, the complexity of fabric textures, and the concealment of small target defects, current fabric defect detection algorithms suffer from issues such as having a slow detection speed, low detection accuracy, and a low recognition rate of small target defects. Therefore, developing an efficient and accurate fabric defect detection system has become an urgent problem that needs to be addressed in the textile industry. Addressing the aforementioned issues, this paper proposes an improved YOLOv8n-LAW algorithm based on the YOLOv8n algorithm. First, LSKNet attention mechanisms are added to both ends of the C2f module in the backbone network to provide a broader context area, enhancing the algorithm’s feature extraction capability. Next, the PAN-FPN structure of the backbone network is replaced by the AFPN structure, so that the different levels of features of the defects are closer to the semantic information in the progressive fusion. Finally, the CIoU loss is replaced with the WIoU v3 loss, allowing the model to dynamically adjust gradient gains based on the features of fabric defects, effectively focusing on distinguishing between defective and non-defective regions. The experimental results show that the improved YOLOv8n-LAW algorithm achieved an accuracy of 97.4% and a detection speed of 46 frames per second, while effectively increasing the recognition rate of small target defects.

In this study, a method of preparing a Ni-P-nanoPTFE composite coating on the surface of GCr15 steel for spinning rings is proposed. The method incorporates a defoamer into the plating solution to inhibit the agglomeration of nano-PTFE particles and pre-deposits a Ni-P transition layer to reduce the possibility of leakage coating. Meanwhile, the effect of varying the PTFE emulsion content in the bath on the micromorphology, hardness, deposition rate, crystal structure, and PTFE content of the composite coatings was investigated. The wear and corrosion resistances of the GCr15 substrate, Ni-P coating, and Ni-P-nanoPTFE composite coating are compared. The results show that the composite coating prepared at a PTFE emulsion concentration of 8 mL/L has the highest concentration of PTFE particles (up to 2.16 wt%). Additionally, its wear resistance and corrosion resistance are improved compared with Ni-P coating. The friction and wear study shows that the nano-PTFE particles with low dynamic friction coefficient are mixed in the grinding chip, which gives the composite coating self-lubricating characteristics, and the friction coefficient decreases to 0.3 compared with 0.4 of Ni-P coating. The corrosion study shows that the corrosion potential of the composite coating has increased by 7.6% compared with that of the Ni-P coating, which shifts from −456 mV to a more positive value of −421 mV. The corrosion current reduces from 6.71 μA to 1.54 μA, which is a 77% reduction. Meanwhile, the impedance increased from 5504 Ω·cm² to 36,440 Ω·cm², which is an increase of 562%.