Milinda Priyankara Bandara Gamawelagedara¹, Mian Usman Sattar¹, Raza Hasan^2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4061-4084, 2025, DOI:10.32604/cmc.2025.062826 - 19 May 2025

Abstract Fuel station drive-offs, wherein the drivers simply drive off without paying, are a major issue in the UK (United Kingdom) due to rising fuel costs and financial hardships. The phenomenon has increased greatly over the last few years, with reports indicating a substantial increase in such events in the major cities. Traditional prevention measures such as Avutec and Driveoffalert rely primarily on expensive infrastructure and blacklisted databases. Such systems typically involve costly camera installation and maintenance and are consequently out of the budget of small fuel stations. These conventional approaches also fall short regarding real-time… More >

Praveen Kumar Sekharamantry^1,2,*, Farid Melgani¹, Roberto Delfiore³, Stefano Lusardi³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4215-4238, 2025, DOI:10.32604/cmc.2025.062686 - 19 May 2025

Abstract Recent advances in computer vision and artificial intelligence (AI) have made real-time people counting systems extremely reliable, with experts in crowd control, occupancy supervision, and security. To improve the accuracy of people counting at entry and exit points, the current study proposes a deep learning model that combines You Only Look Once (YOLOv8) for object detection, ByteTrack for multi-object tracking, and a unique method for vector-based movement analysis. The system determines if a person has entered or exited by analyzing their movement concerning a predetermined boundary line. Two different logical strategies are used to record… More >

Xianghong Cao, Chenxu Li^*, Haoting Zhai

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5487-5515, 2025, DOI:10.32604/cmc.2025.061823 - 19 May 2025

Abstract The behavior safety testing of more and more elderly people living alone has become a hot research topic along with the arrival of an aging society. A YOLO-Abnormal Behaviour (YOLO-AB) algorithm for fusion detection of falling and smoking behaviors of elderly people living alone has been proposed in this paper, which can fully utilize the potential of the YOLOv8 algorithm on object detection and deeply explore the characteristics of different types of behaviors among the elderly, to solve the problems of single detection type, low fusion detection accuracy, and high missed detection rate. Firstly, datasets… More >

Aqsa Rahim^*, Fuqing Yuan, Javad Barabady

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4411-4428, 2025, DOI:10.32604/cmc.2025.061575 - 19 May 2025

Abstract In recent years, advancements in autonomous vehicle technology have accelerated, promising safer and more efficient transportation systems. However, achieving fully autonomous driving in challenging weather conditions, particularly in snowy environments, remains a challenge. Snow-covered roads introduce unpredictable surface conditions, occlusions, and reduced visibility, that require robust and adaptive path detection algorithms. This paper presents an enhanced road detection framework for snowy environments, leveraging Simple Framework for Contrastive Learning of Visual Representations (SimCLR) for Self-Supervised pretraining, hyperparameter optimization, and uncertainty-aware object detection to improve the performance of You Only Look Once version 8 (YOLOv8). The model… More >

Junzhe Zhang¹, Lei Tang^1,*, Xin Zhou²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2643-2658, 2025, DOI:10.32604/cmc.2025.061690 - 16 April 2025

Abstract Fisheye cameras offer a significantly larger field of view compared to conventional cameras, making them valuable tools in the field of computer vision. However, their unique optical characteristics often lead to image distortions, which pose challenges for object detection tasks. To address this issue, we propose Yolo-CaSKA (Yolo with Contrastive Learning and Selective Kernel Attention), a novel training method that enhances object detection on fisheye camera images. The standard image and the corresponding distorted fisheye image pairs are used as positive samples, and the rest of the image pairs are used as negative samples, which More >

Lieping Zhang^1,2, Hao Ma¹, Jiancheng Huang³, Cui Zhang^4,*, Xiaolin Gao²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2245-2265, 2025, DOI:10.32604/cmc.2025.061519 - 16 April 2025

Abstract Detecting individuals wearing safety helmets in complex environments faces several challenges. These factors include limited detection accuracy and frequent missed or false detections. Additionally, existing algorithms often have excessive parameter counts, complex network structures, and high computational demands. These challenges make it difficult to deploy such models efficiently on resource-constrained devices like embedded systems. Aiming at this problem, this research proposes an optimized and lightweight solution called FGP-YOLOv8, an improved version of YOLOv8n. The YOLOv8 backbone network is replaced with the FasterNet model to reduce parameters and computational demands while local convolution layers are added.… More >

Honglin Wang¹, Yaolong Zhang^2,*, Cheng Zhu³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1929-1949, 2025, DOI:10.32604/cmc.2025.061363 - 16 April 2025

Abstract UAV-based object detection is rapidly expanding in both civilian and military applications, including security surveillance, disaster assessment, and border patrol. However, challenges such as small objects, occlusions, complex backgrounds, and variable lighting persist due to the unique perspective of UAV imagery. To address these issues, this paper introduces DAFPN-YOLO, an innovative model based on YOLOv8s (You Only Look Once version 8s). The model strikes a balance between detection accuracy and speed while reducing parameters, making it well-suited for multi-object detection tasks from drone perspectives. A key feature of DAFPN-YOLO is the enhanced Drone-AFPN (Adaptive Feature… More >

Zhuowei Wang^1,*, Dezhi Han¹, Bing Han², Zhongdai Wu²

Computer Systems Science and Engineering, Vol.49, pp. 377-399, 2025, DOI:10.32604/csse.2025.059634 - 09 April 2025

Abstract Synthetic Aperture Radar (SAR) has become one of the most effective tools in ship detection. However, due to significant background interference, small targets, and challenges related to target scattering intensity in SAR images, current ship target detection faces serious issues of missed detections and false positives, and the network structures are overly complex. To address this issue, this paper proposes a lightweight model based on YOLOv8, named OD-YOLOv8. Firstly, we adopt a simplified neural network architecture, VanillaNet, to replace the backbone network, significantly reducing the number of parameters and computational complexity while ensuring accuracy. Secondly,… More >

Nizar Zaghden¹, Emad Ibrahim², Mukaram Safaldin^2,*, Mahmoud Mejdoub³

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1117-1147, 2025, DOI:10.32604/cmc.2025.061948 - 26 March 2025

Abstract The increasing elderly population has heightened the need for accurate and reliable fall detection systems, as falls can lead to severe health complications. Existing systems often suffer from high false positive and false negative rates due to insufficient training data and suboptimal detection techniques. This study introduces an advanced fall detection model integrating YOLOv8, Faster R-CNN, and Generative Adversarial Networks (GANs) to enhance accuracy and robustness. A modified YOLOv8 architecture serves as the core, utilizing spatial attention mechanisms to improve critical image regions’ detection. Faster R-CNN is employed for fine-grained human posture analysis, while GANs… More >

Pengcheng Gao^*, Zhenjiang Li

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4555-4572, 2025, DOI:10.32604/cmc.2025.060873 - 06 March 2025

Abstract The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles (UAV) has emerged as a prominent research focus. Due to the considerable distance between UAVs and the photographed objects, coupled with complex shooting environments, existing models often struggle to achieve accurate real-time target detection. In this paper, a You Only Look Once v8 (YOLOv8) model is modified from four aspects: the detection head, the up-sampling module, the feature extraction module, and the parameter optimization of positive sample screening, and the YOLO-S3DT model is proposed to improve the performance of More >