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An End-to-End Classification Network Model Based on Hybrid Supervision for Industrial Surface Defect Detection

International Journal of Pattern Recognition and Artificial Intelligence 2025年第5期39卷

作者： Qin, Runbing Chen, Ningjiang Gan, Shukun Guangxi University School of Computer Electronics and Information Nanning530004 China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Nanning530004 China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning530004 China

Industrial part surface defect detection aims to precisely locate defects in images, which is crucial for quality control in manufacturing. The traditional method needs to be designed in advance, but it has shortcomings in terms of generalization ability. Deep neural network-based defect detection faces issues such as low resolution, data scarcity, labeling costs, and high computation. Therefore, an improved solution is necessary to enhance industrial product quality control efficiency and accuracy. A new method is proposed that utilizes end-to-end training of a two-stage neural network based on segmentation with an extended training process. The gradient flow from the classification to the segmentation network is adjusted to prevent unstable features from interfering with learning. To address the problem of image oversampling and undersampling during training, a frequency sampling scheme for negative samples is introduced. Additionally, positive pixels in the region-based segmentation mask are weighted using the distance transform algorithm so that regions with a high probability of defects can be detected without detailed annotation. Experiments are conducted across three distinct defect datasets by applying hybrid supervision encompassing diverse conditions. In the optimal case, the AP rate of the proposed model on the DAGM and KolektorSDD datasets reaches 100%, and the AP rate on the Severstal Steel dataset reaches 98.99%. The experiments’ results show that the detection accuracy has improved greatly, showing how well the proposed method works in the industry. © 2025 World Scientific Publishing Company.

关键词： Deep neural networks

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PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning

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Ad Hoc Networks 2025年 176卷

作者： Ou, Pingjie Chen, Ningjiang Yang, Long School of Computer and Electronic Information Guangxi University Nanning Guangxi530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Nanning Guangxi530004 China The Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning Guangxi530004 China

In vehicular networks, onboard devices face the challenge of limited storage, and computational resources constrain their processing and storage capabilities. This limitation is particularly significant for applications that require complex computations and real-time responses. Additionally, limited storage capacity reduces the range of cacheable data, which can impact the immediate availability of data and the continuity of services. Therefore, improving cache utilization and meeting vehicles’ real-time data demands pose significant challenges. Deep reinforcement learning can optimize the issues arising from agents’ continuously changing state and action spaces due to increasing request demands. However, training the network may encounter instability and convergence difficulties in dynamic and complex environments or situations with sparse rewards. In response to these issues, this paper proposes a Priority-based Deep Reinforcement Learning Collaborative Cache Management method (PDRL-CM). PDRL-CM first designs a lightweight cache admission strategy that leverages data's inherent and combined attributes. It then makes cache admission decisions Using Monte Carlo sampling and a max-value search strategy combined with a feedforward neural network. Secondly, the method considers minimizing system latency and reducing vehicle energy consumption as joint optimization problems. An improved deep reinforcement learning algorithm solves this problem and makes cache-sharding decisions. A prioritized experience replay mechanism is incorporated to adjust the network prediction model quickly and accelerate the convergence process. Experimental results indicate that, compared to existing DRL-based caching methods, PDRL-CM offers faster data processing efficiency and higher cache hit rates under varying vehicle density, storage capacity, and content volume conditions. © 2025

关键词： Cache memory

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PERFORMANCE OF parallel SPANNING TREE ALGORITHMS ON LINEAR ARRAYS OF TRANSPUTERS AND UNIX SYSTEMS

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parallel computing 1991年第4-5期17卷 527-551页

作者： DAS, SK YANG, CQ Center for Research in Parallel and Distributed Computing Department of Computer Sciences University of North Texas P.O. Box 13886 Denton TX 76203-3886 USA

This paper presents empirical performance of parallel algorithms for connected graphs on the transputer and Unix systems, where processes are configured as a one-dimensional array. We conduct experiments for implementing (i) spanning tree algorithm (SPT-list) with unordered list of edges as input, (ii) SPT algorithm with adjacency matrix, (iii) minimum spanning tree algorithm (MST) with weight matrix as input, and (iv) SPT algorithm derived from MST algorithm by assigning each edge-weight equal to 1. The empirical study is performed with a wide range of random graphs, generated for various (uniformly distributed) edge-densities (d) for a given number (n) of vertices. We plot curves for resulting speedups as functions of n, d, and p. The edge-density d is varied between 0.1 and 0.9;maximum number of vertices (or edges) considered are 300 (or 40 000) and 500 (or 110 000) for transputer and Unix systems, respectively;and the number of processors p varies from 1 through 8 in the transputer system while 1 through 4 in the Unix system. A maximum speedup of 2.98 is obtained on transputers, and that for the Unix system is 3.0. We observe that the speedups of all algorithms vary with increasing number of vertices or edge-density. However, employing more processing units in an algorithm does not necessarily enhance its speedup because of additional communication overhead.

关键词： TRANSPUTERS LINEAR ARRAY PERFORMANCE STUDY parallel GRAPH ALGORITHM SPANNING TREE MINIMUM SPANNING TREE

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On the Complexity of Learning with a Small Number of Nodes

On the Complexity of Learning with a Small Number of Nodes

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1992 International Joint Conference on Neural Networks, IJCNN 1992

作者： Parberry, Ian Center for Research in Parallel and Distributed Computing Department of Computer Sciences University of North Texas United States

ISBN: (纸本)0780305590

It is shown that the loading pioblem foi a 6 node neural netwoik with node function set AC^ (that is, the conjunction or disjunction of a subset of the inputs or their complements) is AiV complete. It can be deduced from this observation that the loading problem for a 6 node analog neural network is AfV hard. ©1992 IEEE

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Efficient parallel algorithms on interval graphs 4th

Efficient parallel algorithms on interval graphs

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4th International parallel Architectures and Languages Europe Conference, PARLE 1992

作者： Das, Sajal K. Chen, Calvin C. -Y. Center for Research in Parallel and Distributed Computing Department of Computer Science University of North Texas DentonTX76203 United States

ISBN: (纸本)9783540555995

We design efficient parallel algorithms for solving several problems on interval graphs. The problems include finding a BFS-tree and DFS-tree, articulation points and bridges, and minimum coloring. Each of our algorithms requires O(log n) time employing O(n) processors on the EREW PRAM model, where n is the number of vertices. The proposed algorithms for computing articulation points and bridges have better performance in terms of the cost (i.e, processor-time product) than the existing algorithms in [RR90], yet having the same time complexity. Our novel approach to the construction of BFS tree is based on elegantly capturing the structure of a given collection of intervals. This structure reveals important properties of the corresponding interval graph, and is found to be instrumental in solving many other problems on such graphs. Although the time- and processor-complexities of the proposed DFS-tree construction or minimum-coloring is comparable with the best-known ones [KI89], our approach to these problems are new. For example, the approach to the construction of a DFS tree is based on reducing the problem into an all dominating neighbors (ADN) problem;while that to the minimum coloring is by transforming this problem into a linked list ranking problem. © Springer-Verlag Berlin Heidelberg 1992.

关键词： parallel algorithms

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Managing a parallel heap efficiently 3rd

Managing a parallel heap efficiently

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3rd International Conference on parallel Architectures and Languages Europe, PARLE 1991

作者： Das, Sajal K. Horng, Wen-Bing Center for Research in Parallel and Distributed Computing Department of Computer Science University of North Texas DentonTX76203-3886 United States

ISBN: (纸本)9783540541516

We design a cost-optimal algorithm for managing a parallel heap on an exclusive-read and exclusive-write (EREW), parallel random access machine (PRAM) model. This is an improvement in space and time over the one recently proposed by Deo and Prasad [4]. Our approach efficiently employs p processors in the range 1 ≤ p ≤ n, where n is the maximum number of items in a parallel heap. It is assumed that a delete-think-insert cycle is repeatedly performed, and each processor requires an arbitrary but the same amount of time (called the think time) for processing an item which in turn generates at most α (a constant) new items. The time required for deleting p items of the highest priority from the parallel heap is O(l), while that for inserting at most αp new items is 0(logn). With or without incorporating the think time, the speedup of our algorithm is proved to be linear, i.e. 0(p). Using a global, working data structure for each level of the heap, it is shown that the additional memory space required for our parallel heap is much less than that for the existing one [4]. Furthermore, the proposed algorithm retains the strict priority ordering of a sequential heap. © Springer-Verlag Berlin Heidelberg 1991.

关键词： Data structures

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Cost-optimal parallel algorithms for finitely inductive sequences 26

Cost-optimal parallel algorithms for finitely inductive sequ...

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26th Hawaii International Conference on System sciences, HICSS 1993

作者： Das, S.K. Fisher, P.S. Zhang, H. Center for Research in Parallel and Distributed Computing Department of Computer Science University of North Texas DentonTX76203-3886 United States

ISBN: (纸本)0818632305

Finitely inductive (FI) sequences are a class of sequences, finite or infinite, which are amenable to a certain mathematical representation which has direct significance to pattern recognition and string matching. Pattern recognition using the FI technique first transforms known patterns into function table(s) by factoring, and then uses the function iable(s) to match an input pattern by following. Factoring is done off line and only once for each pattern. The sequential time for matching two sequences depends only upon the length of the input sequence. In this paper, we propose cost-optimal (i.e., efficient) parallel algorithms for FI sequence processing. These algorithms include parallel factoring and following by bucket packing and by hashing on an exclusive-read and exclusive-write (EREW), parallel random access machine (PRAM) model. © 1993 IEEE.

关键词： Pattern recognition

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Performance of parallel spanning tree algorithms on linear arrays of transputers and Unix systems 5

Performance of parallel spanning tree algorithms on linear a...

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5th International parallel Processing Symposium, IPPS 1991

作者： Das, Sajal K. Yang, Cui-Qing Center for Research in Parallel and Distributed Computing Department of Computer Sciences University of North Texas DentonTX76203-3886 United States

ISBN: (纸本)0818691670

This paper presents empirical performance of parallel algorithms for computing a spanning tree (SPT) and a minimum spanning tree (MST) of connected graphs on the Transputer and Unix systems, where processors are configured as a onedimensional array. The parallel MST algorithm uses a weight matrix data structure;and three implementations of the SPT algorithm are presented with unordered edge-list, linked adjacency list and adjacency matrix as data structures. The experiments are conducted with a wide range of random graphs, generated for various edge-densities (d) for a given number (n) of vertices. The edge-density is varied between 0.1 and 0.9, and the maximum number of vertices (or edges) considered are 300 (or 40,000) and 500 (or 110,000) for transputer and Unix systems, respectively. A maximum speed-up of 2.98 is achieved on the transputer network of 8 processors, and that for the Unix system is 3.0 with 4 processes. © 1991 IEEE.

关键词： Transputers

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A cost-optimal parallel algorithm for the parentheses matching problem on an EREW PRAM 5

A cost-optimal parallel algorithm for the parentheses matchi...

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5th International parallel Processing Symposium, IPPS 1991

作者： Chen, Calvin C.-Y. Das, Sajal K. Center for Research in Parallel and Distributed Computing Department of Computer Sciences University of North Texas DentonTX76203-3886 United States

We present a cost-optimal parallel algorithm for the parentheses matching problem on the EREW PRAM model For n parentheses, the algorithm requires O(n/p + log n) time and O(n + p log p) space, employing p processors. ... 详细信息

ISBN: (纸本)0818691670

关键词： Computational complexity

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Video-based hypermedia for education-on-demand 96

Video-based hypermedia for education-on-demand

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4th ACM International Conference on Multimedia, MULTIMEDIA 1996

作者： Ma, Wei-Hsiu Lee, Yen-Jen Du, David H.C. McCahill, Mark P. Distributed Multimedia Research Center Department of Computer Science University of Minnesota United States Distributed Computing Services University of Minnesota United States

No abstract available.

ISBN: (纸本)0897918711

No abstract available.

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