Zhe Chen^*, Yinyang Zhang, Sihao Xing

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1787-1803, 2025, DOI:10.32604/cmc.2025.065238 - 09 June 2025

Abstract Unmanned aerial vehicle (UAV) imagery poses significant challenges for object detection due to extreme scale variations, high-density small targets (68% in VisDrone dataset), and complex backgrounds. While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion, their rigid architectures struggle with multi-scale adaptability, as exemplified by YOLOv8n’s 36.4% mAP and 13.9% small-object AP on VisDrone2019. This paper presents YOLO-LE, a lightweight framework addressing these limitations through three novel designs: (1) We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters, thereby improving More >

Van-Ha Hoang¹, Jong Weon Lee¹, Chun-Su Park^2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4097-4116, 2025, DOI:10.32604/cmc.2025.063468 - 19 May 2025

Abstract Fire can cause significant damage to the environment, economy, and human lives. If fire can be detected early, the damage can be minimized. Advances in technology, particularly in computer vision powered by deep learning, have enabled automated fire detection in images and videos. Several deep learning models have been developed for object detection, including applications in fire and smoke detection. This study focuses on optimizing the training hyperparameters of YOLOv8 and YOLOv10 models using Bayesian Tuning (BT). Experimental results on the large-scale D-Fire dataset demonstrate that this approach enhances detection performance. Specifically, the proposed approach… More >

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 >

Hoang Thi Minh Chau^1,2,3, Tran Thi Ngan^4,*, Nguyen Long Giang⁵, Tran Manh Tuan⁶, Tran Kim Chau⁷

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4915-4937, 2025, DOI:10.32604/cmc.2025.062784 - 19 May 2025

Abstract Global climate change, along with the rapid increase of the population, has put significant pressure on water security. A water reservoir is an effective solution for adjusting and ensuring water supply. In particular, the reservoir water level is an essential physical indicator for the reservoirs. Forecasting the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management policies. In recent years, deep learning models have been widely applied to solve forecasting problems. In this study, we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9,… 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 >

Zijun Gao^*, Zheyi Li, Chunqi Zhang, Ying Wang, Jingwen Su

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4353-4371, 2025, DOI:10.32604/cmc.2025.061743 - 19 May 2025

Abstract Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks; however, their application in the actual agricultural production process is still challenging owing to the problems of inter-species similarity, multi-scale, and background complexity of pests. To address these problems, this study proposes an FD-YOLO pest target detection model. The FD-YOLO model uses a Fully Connected Feature Pyramid Network (FC-FPN) instead of a PANet in the neck, which can adaptively fuse multi-scale information so that the model can retain small-scale target features in the deep layer, enhance large-scale target features in the… 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 >

Yung-Hsiang Hung, Chuen-Kai Fan, Wen-Pai Wang^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2323-2349, 2025, DOI:10.32604/cmc.2025.063270 - 16 April 2025

Abstract Bees play a crucial role in the global food chain, pollinating over 75% of food and producing valuable products such as bee pollen, propolis, and royal jelly. However, the Asian hornet poses a serious threat to bee populations by preying on them and disrupting agricultural ecosystems. To address this issue, this study developed a modified YOLOv7tiny (You Only Look Once) model for efficient hornet detection. The model incorporated space-to-depth (SPD) and squeeze-and-excitation (SE) attention mechanisms and involved detailed annotation of the hornet’s head and full body, significantly enhancing the detection of small objects. The Taguchi… 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 >