Fabric defect detection is a key part of product quality assessment in the textile industry. It is important to achieve fast, accurate and efficient detection of fabric defects to improve productivity in the textile industry. For the problems of irregular shapes and many small objects, an improved YOLOv5 object detection algorithm for fabric defects is propose. In order to improve the detection accuracy of small objects, the ASFF(Adaptively Spatial Feature Fusion) feature fusion method is adopted to improve the PANet’s bad effect on multi-scale feature fusion. The transformer mechanisms can enhance fused features, allowing the network to focus on useful information. Experimental results show that the mean average precision of the improved YOLOv5 object detection algorithm in fabric defect map detection is 71.70%. The improved algorithm can quickly and accurately improve the accuracy of fabric defect detection and the accuracy of defect localization.

In this paper, we formulate and solve the selfish allocation problem by using game theory, which is different from the previously studied researches in three ways that make it more accurately reflective of real world peer-to-peer (P2P) allocation: (i) we treat the nodes as strategic agents and treat the replica allocation as a deliberate auction where node is incentivized to give his true quality of service for obtaining the replica; (ii) our mechanism computes node utility for all possible replica destination and payments for those destination nodes, and the best appropriate node can be selected as the final placement destination; and (iii) we show how to carry out our scheme with a distributed algorithm that is a straightforward extension to P2P allocation method and causes an overhead in convergence time. Our design and analysis of a strategy proof, feasible, Vickrey-Clarke-Groves-based auction scheme provides a new, promising direction in distributed algorithmic mechanism design, which has heretofore been focused mainly on P2P application. Copyright

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Privacy-preserving data aggregation is an important technology for mobile crowdsensing. Blockchain-assisted data aggregation enables the traceability of sensing data to improve the trustworthiness of data aggregation results. However, directly using blockchains for data aggregation may introduce the risk of privacy leakage because all nodes, including malicious nodes, can access the data on blockchains. In this paper, we propose a grouping-based reliable privacy-preserving data aggregation (RPPDA) method using private blockchains for mobile crowdsensing. First, the sensing nodes are divided into multiple groups, and each group maintains a private blockchain to store the data aggregation records, which avoids the leakage of the aggregated results and ensures the traceability of the sensory data. Then, a zero-sum noise-adding mechanism is utilized to not only preserve the private information during aggregation and ensure the correctness of the aggregated results but also improve the efficiency of privacy preservation. Furthermore, we theoretically prove the correctness, privacy, efficiency, and reliability of the proposed RPPDA algorithm. Real-world and simulated experiments demonstrate the effectiveness and advantages of the proposed RPPDA algorithm in terms of correctness, efficiency, and privacy.

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