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A Study on Enhancing Chip Detection Efficiency Using the lightweight Van-YOLOv8 Network

Computers, Materials & Continua 2024年第4期79卷 531-547页

作者： Meng Huang Honglei Wei Xianyi Zhai School of Mechanical Engineering and Automation Dalian Polytechnic UniversityDalian116034China

In pursuit of cost-effective manufacturing,enterprises are increasingly adopting the practice of utilizing recycled semiconductor *** ensure consistent chip orientation during packaging,a circular marker on the front side is employed for pin alignment following successful functional ***,recycled chips often exhibit substantial surface wear,and the identification of the relatively small marker proves ***,the complexity of generic target detection algorithms hampers seamless *** these issues,this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips,termed ***,to alleviate the influence of diminutive,low-resolution markings on the precision of deep learning models,we utilize an upscaling approach for enhanced *** technique relies on the Super-Resolution Generative Adversarial Network with Extended Training(SRGANext)network,facilitating the reconstruction of high-fidelity images that align with input ***,we replace the original YOLOv8smodel’s backbone feature extraction network with the lightweight VanillaNetwork(VanillaNet),simplifying the branch structure to reduce network ***,a Hybrid Attention Mechanism(HAM)is implemented to capture essential details from input images,improving feature representation while concurrently expediting model inference *** results demonstrate that the Van-YOLOv8 network outperforms the original YOLOv8s on a recycled chip dataset in various ***,it demonstrates superiority in parameter count,computational intricacy,precision in identifying targets,and speed when compared to certain prevalent algorithms in the current *** proposed approach proves promising for real-time detection of recycled chips in practical factory settings.

关键词： lightweight neural networks attention mechanisms image super-resolution enhancement feature extraction small object detection

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A GAN-EfficientNet-Based Traceability Method for Malicious Code Variant Families

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Computers, Materials & Continua 2024年第7期80卷 801-818页

作者： Li Li Qing Zhang Youran Kong School of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China

Due to the diversity and unpredictability of changes in malicious code,studying the traceability of variant families remains *** this paper,we propose a GAN-EfficientNetV2-based method for tracing families of malicious code *** method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code *** method includes a lightweight classifier and a *** classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile,embedded,and other *** simulator utilizes an enhanced generative adversarial network to simulate different variants of malicious code and generates datasets to validate the model’s *** process helps identify model vulnerabilities and security risks,facilitating model enhancement and *** classifier achieves 98.61%and 97.59%accuracy on the MMCC dataset and Malevis dataset,*** simulator’s generated image of malicious code variants has an FID value of 155.44 and an IS value of 1.72±*** classifier’s accuracy for tracing the family of malicious code variants is as high as 90.29%,surpassing that of mainstream neural network *** meets the current demand for high generalization and anti-obfuscation abilities in malicious code classification models due to the rapid evolution of malicious code.

关键词： Malicious code variant traceability feature reuse lightweight neural networks code visualization attention mechanism

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