Ayman Noor^1,2, Hanan Almukhalfi^1,2, Arthur Souza^2,3, Talal H. Noor^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3085-3111, 2025, DOI:10.32604/cmes.2025.068393 - 30 September 2025

Abstract People with visual impairments face substantial navigation difficulties in residential and unfamiliar indoor spaces. Neither canes nor verbal navigation systems possess adequate features to deliver real-time spatial awareness to users. This research work represents a feasibility study for the wearable IoT-based indoor object detection assistant system architecture that employs a real-time indoor object detection approach to help visually impaired users recognize indoor objects. The system architecture includes four main layers: Wearable Internet of Things (IoT), Network, Cloud, and Indoor Object Detection Layers. The wearable hardware prototype is assembled using a Raspberry Pi 4, while the… More >

Atta Rahman^*, Fahad Abdullah Alatallah, Abdullah Jafar Almubarak, Haider Ali Alkhazal, Hasan Ali Alzayer, Younis Zaki Shaaban, Nasro Min-Allah, Aghiad Bakry, Khalid Aloup

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3507-3525, 2025, DOI:10.32604/cmc.2025.067547 - 23 September 2025

Abstract This study presents an automated system for monitoring Personal Protective Equipment (PPE) compliance using advanced computer vision techniques in industrial settings. Despite strict safety regulations, manual monitoring of PPE compliance remains inefficient and prone to human error, particularly in harsh environmental conditions like in Saudi Arabia’s Eastern Province. The proposed solution leverages the state-of-the-art YOLOv11 deep learning model to detect multiple safety equipment classes, including safety vests, hard hats, safety shoes, gloves, and their absence (no_hardhat, no_safety_vest, no_safety_shoes, no_gloves) along with person detection. The system is designed to perform real-time detection of safety gear while… More >

Hao Sun^1,2, Di Cai¹, Dae-Ki Kang^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1675-1692, 2025, DOI:10.32604/cmc.2025.067675 - 29 August 2025

Abstract Accurately identifying crop pests and diseases ensures agricultural productivity and safety. Although current YOLO-based detection models offer real-time capabilities, their conventional convolutional layers involve high computational redundancy and a fixed receptive field, making it challenging to capture local details and global semantics in complex scenarios simultaneously. This leads to significant issues like missed detections of small targets and heightened sensitivity to background interference. To address these challenges, this paper proposes a lightweight adaptive detection network—StarSpark-AdaptiveNet (SSANet), which optimizes features through a dual-module collaborative mechanism. Specifically, the StarNet module utilizes Depthwise separable convolutions (DW-Conv) and dynamic… More >

Nayeemul Islam Nayeem¹, Shirin Mahbuba¹, Sanjida Islam Disha¹, Md Rifat Hossain Buiyan¹, Shakila Rahman^1,*, M. Abdullah-Al-Wadud², Jia Uddin^3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1541-1557, 2025, DOI:10.32604/cmc.2025.065061 - 29 August 2025

Abstract Human activity recognition is a significant area of research in artificial intelligence for surveillance, healthcare, sports, and human-computer interaction applications. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The dataset consists of 14,186 images across 19 activity classes, from dynamic activities such as running and swimming to static activities such as sitting and sleeping. Preprocessing included resizing all images to 512 512 pixels, annotating them… More >

Zhipeng Qin^1,2,*, Hanbing Yan³, Biyang Zhang², Peng Wang², Yitao Li³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5811-5829, 2025, DOI:10.32604/cmc.2025.063308 - 19 May 2025

Abstract With the widespread adoption of encrypted Domain Name System (DNS) technologies such as DNS over Hyper Text Transfer Protocol Secure (HTTPS), traditional port and protocol-based traffic analysis methods have become ineffective. Although encrypted DNS enhances user privacy protection, it also provides concealed communication channels for malicious software, compelling detection technologies to shift towards statistical feature-based and machine learning approaches. However, these methods still face challenges in real-time performance and privacy protection. This paper proposes a real-time identification technology for encrypted DNS traffic with privacy protection. Firstly, a hierarchical architecture of cloud-edge-end collaboration is designed, incorporating More >

Shanmeng Zhao^1,2, Yaxue Peng^1,*, Yaqing Wang³, Gang Li^3,*, Mohammed Al-Mahbashi¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4995-5017, 2025, DOI:10.32604/cmc.2025.059972 - 06 March 2025

Abstract In recent years, the country has spent significant workforce and material resources to prevent traffic accidents, particularly those caused by fatigued driving. The current studies mainly concentrate on driver physiological signals, driving behavior, and vehicle information. However, most of the approaches are computationally intensive and inconvenient for real-time detection. Therefore, this paper designs a network that combines precision, speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion. Specifically, the face detection model takes YOLOv8 (You Only Look Once version 8) as the basic framework, and replaces its backbone network… More >

Yahia Said^1,2,*, Yahya Alassaf³, Refka Ghodhbani⁴, Taoufik Saidani⁴, Olfa Ben Rhaiem⁵

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3005-3018, 2025, DOI:10.32604/cmc.2025.060928 - 17 February 2025

Abstract Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic… More >

Xin Leng^#, Jiakai Chen^#, Jianping Huang^*, Lei Zhang, Zongxuan Li

Phyton-International Journal of Experimental Botany, Vol.93, No.11, pp. 2963-2981, 2024, DOI:10.32604/phyton.2024.057166 - 30 November 2024

Abstract Tea, a globally cultivated crop renowned for its unique flavor profile and health-promoting properties, ranks among the most favored functional beverages worldwide. However, diseases severely jeopardize the production and quality of tea leaves, leading to significant economic losses. While early and accurate identification coupled with the removal of infected leaves can mitigate widespread infection, manual leaves removal remains time-consuming and expensive. Utilizing robots for pruning can significantly enhance efficiency and reduce costs. However, the accuracy of object detection directly impacts the overall efficiency of pruning robots. In complex tea plantation environments, complex image backgrounds, the… More >

Chengjun Wang^1,2, Fan Ding^2,*, Yiwen Wang¹, Renyuan Wu¹, Xingyu Yao², Chengjie Jiang¹, Liuyi Ling¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1481-1501, 2024, DOI:10.32604/cmc.2023.046876 - 30 January 2024

Abstract The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots. Real-time identification of strawberries in an unstructured environment is a challenging task. Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy. To this end, the present study proposes an Efficient YOLACT (E-YOLACT) algorithm for strawberry detection and segmentation based on the YOLACT framework. The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism, pyramid squeeze shuffle attention (PSSA), for efficient feature extraction. Additionally, an attention-guided… More >

Chao Dong, Xiangkui Jiang^*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3015-3036, 2023, DOI:10.32604/cmc.2023.044735 - 26 December 2023

Abstract To address the issue of imbalanced detection performance and detection speed in current mainstream object detection algorithms for optical remote sensing images, this paper proposes a multi-scale object detection model for remote sensing images on complex backgrounds, called DI-YOLO, based on You Only Look Once v7-tiny (YOLOv7-tiny). Firstly, to enhance the model’s ability to capture irregular-shaped objects and deformation features, as well as to extract high-level semantic information, deformable convolutions are used to replace standard convolutions in the original model. Secondly, a Content Coordination Attention Feature Pyramid Network (CCA-FPN) structure is designed to replace the… More >