Syed Sajid Ullah^1,*, Muhammad Zunair Zamir², Ahsan Ishfaq², Salman Khan¹

Journal on Artificial Intelligence, Vol.7, pp. 255-274, 2025, DOI:10.32604/jai.2025.069008 - 29 August 2025

Abstract Accurate vehicle detection is essential for autonomous driving, traffic monitoring, and intelligent transportation systems. This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module, Convolutional Block Attention Module (CBAM), and Deformable Convolutional Networks v2 (DCNv2). The Ghost Module streamlines feature generation to reduce redundancy, CBAM applies channel and spatial attention to improve feature focus, and DCNv2 enables adaptability to geometric variations in vehicle shapes. These components work together to improve both accuracy and computational efficiency. Evaluated on the KITTI dataset, the proposed model achieves 95.4% mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% More >

Pengfei Wang^1,2,3, Jiwu Sun², Lu Lu^1,4, Hongchen Li¹, Hongzhe Liu², Cheng Xu², Yongqiang Liu^1,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2883-2903, 2025, DOI:10.32604/cmc.2025.065267 - 03 July 2025

Abstract Flooding and heavy rainfall under extreme weather conditions pose significant challenges to target detection algorithms. Traditional methods often struggle to address issues such as image blurring, dynamic noise interference, and variations in target scale. Conventional neural network (CNN)-based target detection approaches face notable limitations in such adverse weather scenarios, primarily due to the fixed geometric sampling structures that hinder adaptability to complex backgrounds and dynamically changing object appearances. To address these challenges, this paper proposes an optimized YOLOv9 model incorporating an improved deformable convolutional network (DCN) enhanced with a multi-scale dilated attention (MSDA) mechanism. Specifically,… More >

Shi Luo, Sheng Zheng^*, Yuxin Zhao

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3881-3901, 2024, DOI:10.32604/cmc.2024.056773 - 19 December 2024

Abstract In the age of smart technology, the widespread use of small LCD (Liquid Crystal Display) necessitates pre-market defect detection to ensure quality and reduce the incidence of defective products. Manual inspection is both time-consuming and labor-intensive. Existing methods struggle with accurately detecting small targets, such as point defects, and handling defects with significant scale variations, such as line defects, especially in complex background conditions. To address these challenges, this paper presents the YOLO-DEI (Deep Enhancement Information) model, which integrates DCNv2 (Deformable convolution) into the backbone network to enhance feature extraction under geometric transformations. The model More >

Jielin Jiang^1,2,3,4,*, Chao Cui¹, Xiaolong Xu^1,2,3,4, Yan Cui⁵

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 725-744, 2024, DOI:10.32604/iasc.2024.036897 - 06 September 2024

Abstract In the textile industry, the presence of defects on the surface of fabric is an essential factor in determining fabric quality. Therefore, identifying fabric defects forms a crucial part of the fabric production process. Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types; in addition, their detection efficiency is low, and their detection results are relatively poor. Deep learning-based methods have many advantages in the field of fabric defect detection, however, such methods are less effective in identifying multi-scale fabric defects and defects with complex shapes. Therefore, we propose… More >

Yongliang Yang, Linghua Xu^*, Maolin Luo, Xiao Wang, Min Cao

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2741-2765, 2024, DOI:10.32604/cmc.2024.046768 - 27 February 2024

Abstract Due to the complex environment of the university laboratory, personnel flow intensive, personnel irregular behavior is easy to cause security risks. Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed. Therefore, the current management of personnel behavior mainly relies on institutional constraints, education and training, on-site supervision, etc., which is time-consuming and ineffective. Given the above situation, this paper proposes an improved You Only Look Once version 7 (YOLOv7) to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy. First, to better capture the… More >

Wenran Jia¹, Simin Ma¹, Peng Geng¹, Yan Sun^2,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3393-3411, 2023, DOI:10.32604/cmc.2023.040091 - 08 October 2023

Abstract Retinal vessel segmentation in fundus images plays an essential role in the screening, diagnosis, and treatment of many diseases. The acquired fundus images generally have the following problems: uneven illumination, high noise, and complex structure. It makes vessel segmentation very challenging. Previous methods of retinal vascular segmentation mainly use convolutional neural networks on U Network (U-Net) models, and they have many limitations and shortcomings, such as the loss of microvascular details at the end of the vessels. We address the limitations of convolution by introducing the transformer into retinal vessel segmentation. Therefore, we propose a… More >

Hongrui Zhang¹, Wenxue Wei^{1, *}, Xinguang Xiao¹, Song Yang¹, Wanlu Shao¹

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 457-468, 2020, DOI:10.32604/cmc.2020.06988 - 30 March 2020

Abstract This DC-YOLO Model was designed in order to improve the efficiency for appraising dangerous class of buildings and avoid manual intervention, thereby making the appraisal results more objective. It is an automated method designed based on deep learning and target detection algorithms to appraise the dangerous class of building masonry component. Specifically, it (1) adopted K-means clustering to obtain the quantity and size of the prior boxes; (2) expanded the grid size to improve identification to small targets; (3) introduced in deformable convolution to adapt to the irregular shape of the masonry component cracks. The More >