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Improved Road Damage Detection Algorithm Based on YOLOv8n

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IAENG International Journal of computer science 2024年第11期51卷 1720-1730页

作者： Li, Xudong Zhang, Yujun School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

This paper introduces an advanced road damage detection algorithm that effectively addresses the shortcomings of existing models, including limited detection performance and large parameter sizes, by utilizing the YOLOv8n model. Key enhancements are integrated into the proposed algorithm to bolster its efficacy. First, the ConvNeXt V2 backbone network is integrated to improve the extraction of contextual features, thereby enhancing the effectiveness of road damage detection. Second, the algorithm employs a C2f_GhostNetV2 block structure to strengthen feature representation while simultaneously reducing computational costs. Additionally, PConv is utilized in the neck region to optimize spatial feature extraction, thereby minimizing redundant computations. The experimental results indicate that the proposed algorithm performs effectively on the Chinese subset of the RDD2022 dataset. Specifically, detection accuracy improved by 1.9%, recall by 1.8%, and mAP@0.5 by 2.1%, while the number of parameters decreased by 24% compared to the initial model. The optimized algorithm increases FPS by 4, meeting the dual requirements of mobile devices for accuracy and real-time object detection. © (2024), (International Association of Engineers). All rights reserved.

关键词： Feature extraction

An Improve Grey Wolf Optimizer Algorithm for Traveling Salesman Problems

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IAENG International Journal of computer science 2024年第6期51卷 602-612页

作者： Xu, Zhinan Zhang, Xiaoxia School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

The Traveling Salesman Problem (TSP) seeks the shortest closed tour that visits each city once and returns to the starting city. This problem is NP-hard, so it is not easy to solve using conventional methods. The grey wolf optimization (GWO) algorithm has shown outstanding per- formance in many practical applications. However, it is inclined towards premature convergence. This paper proposes an im- proved GWO (I-GWO) algorithm, which hybridizes GWO with genetic algorithms (GA) for the TSP. The main feature of the I-GWO algorithm is that it can make full use of the advantages of the GWO algorithm and the GA algorithm to make up for their respective shortcomings. Moreover, to make the GWO suitable for solving the TSP, both the 2-opt operator strategy and hamming distance h ave been designed to implement the discrete GWO directly. Additionally, to increase the diversity of solutions by expanding the search space, we present a new population update strategy with crossover and mutation operations in the next iteration. Meanwhile, the integration of the Simulated Annealing (SA) algorithm into the Improved Grey Wolf Optimizer (I-GWO) enhances its local search capabilities. Experimental results show that the I-GWO algorithm competes with established optimal methods for solving the TSP, suggesting its potential for different TSP variants and logistic transport domains. © (2024), (International Association of Engineers). All rights reserved.

关键词： Traveling salesman problem

Multi-lesion Segmentation of Fundus Images using Improved UNet++

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IAENG International Journal of computer science 2024年第10期51卷 1587-1595页

作者： Jiang, Haoyan Zhao, Ji School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Diabetes retinopathy (DR) is one of the complications of diabetes. Early diagnosis of retinopathy is helpful to avoid vision loss or blindness. The difficulty of this task lies in the significant differences in the size and shape of lesions between different DR samples, with a higher proportion of small lesions. We propose a new multi-disease segmentation method based on UNet++ to improve the segmentation accuracy of DR lesions. We chose Resnet50 as the backbone network and introduced a new hybrid residual module to replace the original residual module. At the same time, to compensate for the loss of information in DR small lesions during the feature extraction process, we introduce the Across Feature Map Attention (AFMA) is an auxiliary branch that enhances the segmentation accuracy of small-scale lesions. Finally, in response to the difficulty in extracting DR lesions in shallow models, the model abandoned the deep supervision structure in UNet++. In addition, we use a weighted mixed loss function to train the model. We conducted experiments on IDRID and DDR public datasets, simultaneously segmenting four typical DR lesions. The results on intersection over union (IOU) and dice similarity coefficient (Dice) showed that our method achieved competitive performance compared to other research methods. © (2024), (International Association of Engineers). All rights reserved.

关键词： Semantic Segmentation

PCB Surface Defect Detection based on YOLOv8n

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IAENG International Journal of computer science 2024年第12期51卷 2017-2025页

作者： You, Rui Wang, Zhifeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the electronic manufacturing industry, accurate detection of PCB defects is crucial as it directly impacts product quality and reliability. The primary challenges in PCB defect detection include missed detections and false alarms, particularly concerning micro-defects. This study proposes an enhanced PCB defect detection algorithm based on YOLOv8, which incorporates the Global Attention Module (GAM), Partial Convolution Layer (PConv3), and a multi-head detection strategy. The GAM improves the model’s sensitivity to micro-defects by capturing and weighting global context information through spatial and channel attention mechanisms applied to the input feature map. The PConv3 optimizes feature extraction, minimizing false alarms due to information loss. The multi-head detection strategy identifies defects at varying scales, preserving detailed information and enhancing the detection of small-sized defects. Experimental results demonstrate that the improved algorithm achieves a 3.6% increase in average precision while meeting real-time detection requirements. © (2024), (International Association of Engineers). All rights reserved.

关键词： Deep Learning Global Attention Module Multi-head Detection PCB Defect Detection PConv3 YOLOv8

CLIP-Flow:Decoding images encoded in CLIP space

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Computational Visual Media 2024年第6期10卷 1157-1168页

作者： Hao Ma Ming Li Jingyuan Yang Or Patashnik Dani Lischinski Daniel Cohen-Or Hui Huang Visual Computing Research Center College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060China Department of Computer Science Tel Aviv UniversityTel Aviv 6997801Israel School of Computer Science and Engineering the Hebrew University of JerusalemJerusalem 91904Israel

This study introduces CLIP-Flow,a novel network for generating images from a given image or *** effectively utilize the rich semantics contained in both modalities,we designed a semantics-guided methodology for image-and text-to-image *** particular,we adopted Contrastive Language-Image Pretraining(CLIP)as an encoder to extract semantics and StyleGAN as a decoder to generate images from such ***,to bridge the embedding space of CLIP and latent space of StyleGAN,real NVP is employed and modified with activation normalization and invertible *** the images and text in CLIP share the same representation space,text prompts can be fed directly into CLIP-Flow to achieve text-to-image *** conducted extensive experiments on several datasets to validate the effectiveness of the proposed image-to-image synthesis *** addition,we tested on the public dataset Multi-Modal CelebA-HQ,for text-to-image *** validated that our approach can generate high-quality text-matching images,and is comparable with state-of-the-art methods,both qualitatively and quantitatively.

关键词： image-to-image text-to-image contrastive language-image pretraining(CLIP) flow StyleGAN

Research on Image Defogging Algorithm Based on Improved FFA-Net

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IAENG International Journal of computer science 2024年第6期51卷 634-641页

作者： Qinrong, Li Chi, Ma Qiang, Guo Hui, Hu School of Computer Science and Software Engineering University of Science and Technology LiaoNing AnShan114051 China School of Computer Science and Engineering Huizhou University Huizhou516007 China

Images captured under severe weather conditions, such as haze and fog, suffer from image quality degradation caused by atmospheric particle diffusion. This degradation manifests as color fading, reduced contrast, and adversely affects the performance of various computer vision tasks. To address this, this paper presents an end-to-end feature fusion attention network (FFA-Net) designed to directly restore haze-free images. By incorporating the SSIM loss into the original loss function, the proposed method effectively captures the visual disparities between the estimated defogged image and the authentic haze-free image. Additionally, it mitigates the color distortion problem inherent in the original algorithm. To address the challenge of low brightness in input images, a low illumination enhancement module is introduced, seamlessly integrated with the FFA-Net defogging method. Subsequently, a comparative analysis of different defogging algorithms is conducted using two distinct foggy datasets. Multiple evaluation metrics are employed to assess the performance of these algorithms. The findings indicate that our algorithm significantly outperforms others in terms of objective indicators such as PSNR and SSIM, as well as visual effects. © (2024), (International Association of Engineers). All rights reserved.

关键词： Image enhancement

An Apricot Detection Algorithm in Complex Environments Based on Improved YOLOv7

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IAENG International Journal of computer science 2024年第12期51卷 2135-2144页

作者： Guo, Qiang Ma, Chi Hu, Hui School of Computer Science and Software Engineering University of Science and Technology LiaoNing AnShan114051 China School of Computer Science and Engineering Huizhou University Huizhou516007 China

Apricot detection is a prerequisite for counting and harvesting tasks. Existing algorithms face challenges in adapting to the impacts of complex environmental factors such as lighting variations, shadows, dense foliage, and the uneven distribution of samples in mechanized apricot harvesting. This paper proposes an enhanced model, YOLOv7-DC, based on YOLOv7, to address these challenges. YOLOv7-DC preprocesses diverse apricot tree samples to accommodate real-world harvesting detection scenarios. To improve model inference speed and detection accuracy, the detection network is redesigned with a new feature fusion method. DCNv2 is embedded within the efficient layer aggregation network (ELAN), and PConv is introduced to replace conventional convolutions, reducing the parameter impact of DCNv2. The training process incorporates the CBAM attention mechanism to enhance spatial and channel information. The ConvMixer architecture captures spatial and channel relationships transmitted to the detection head through the attention mechanism, improving the model’s detection accuracy for each specific classification sample. Experimental results show that YOLOv7-DC maintains good detection speed and recognition rates across various classification tasks. The improved model achieves a 6.2% increase in average detection accuracy compared to previous algorithms, with a 13% reduction in model parameters. YOLOv7-DC is better suited for handling imbalanced samples and complex environmental scenarios. © (2024), (International Association of Engineers). All rights reserved.

关键词： Apricot biloba detection Attention mechanism Feature fusion YOLOv7

A probabilistic generative model for tracking multi-knowledge concept mastery probability

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Frontiers of computer science 2024年第3期18卷 155-170页

作者： Hengyu LIU Tiancheng ZHANG Fan LI Minghe YU Ge YU School of Computer Science and Engineering Northeastern UniversityShenyang 110169China Software College Northeastern UniversityShenyang 110169China

Knowledge tracing aims to track students’knowledge status over time to predict students’future performance *** a real environment,teachers expect knowledge tracing models to provide the interpretable result of knowledge *** chain-based knowledge tracing(MCKT)models,such as Bayesian Knowledge Tracing,can track knowledge concept mastery probability over ***,as the number of tracked knowledge concepts increases,the time complexity of MCKT predicting student performance increases exponentially(also called explaining away problem).When the number of tracked knowledge concepts is large,we cannot utilize MCKT to track knowledge concept mastery probability over *** addition,the existing MCKT models only consider the relationship between students’knowledge status and problems when modeling students’responses but ignore the relationship between knowledge concepts in the same *** address these challenges,we propose an inTerpretable pRobAbilistiC gEnerative moDel(TRACED),which can track students’numerous knowledge concepts mastery probabilities over *** solve explain away problem,we design long and short-term memory(LSTM)-based networks to approximate the posterior distribution,predict students’future performance,and propose a heuristic algorithm to train LSTMs and probabilistic graphical model *** better model students’exercise responses,we proposed a logarithmic linear model with three interactive strategies,which models students’exercise responses by considering the relationship among students’knowledge status,knowledge concept,and *** conduct experiments with four real-world datasets in three knowledge-driven *** experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students’future performance and can learn the relationship among students,knowledge concepts,and problems from students’exercise *** also conduct several case *** case studies show that

关键词： probabilistic graphical model deep learning knowledge tracing learner modeling

Designing dataless neural networks for kidney exchange variants

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Neural Computing and Applications 2024年第35期36卷 22265-22275页

作者： Jena, Sangram K. Subramani, K. Velasquez, Alvaro Department of Computer Science and Software Engineering Miami University OxfordOH United States LDCSEE West Virginia University MorgantownWV United States Department of Computer Science University of Colorado Boulder BoulderCO United States

Kidney transplantation is vital for treating end-stage renal disease, impacting roughly one in a thousand Europeans. The search for a suitable deceased donor often leads to prolonged and uncertain wait times, making living donor transplants a viable alternative. However, approximately 40% of living donors are incompatible with their intended recipients. Therefore, many countries have established kidney exchange programs, allowing patients with incompatible donors to participate in "swap" arrangements, exchanging donors with other patients in similar situations. Several variants of the vertex-disjoint cycle cover problem model the above problem, which deals with different aspects of kidney exchange as required. This paper discusses several specific vertex-disjoint cycle cover variants and deals with finding the exact solution. We employ the dataless neural networks framework to establish single differentiable functions for each variant. Recent research highlights the framework’s effectiveness in representing several combinatorial optimization problems. Inspired by these findings, we propose customized dataless neural networks for vertex-disjoint cycle cover variants. We derive a differentiable function for each variant and prove that the function will attain its minimum value if an exact solution is found for the corresponding problem variant. We also provide proof of the correctness of our approach. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Combinatorial optimization