Li Li, Xiao Wang^*, Ran Ding, Linlin Luo, Qinmu Wu, Zhiqin He

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1287-1309, 2025, DOI:10.32604/cmc.2025.065670 - 29 August 2025

Abstract Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues, and their appearance may lead to a series of complex symptoms. However, current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology, size, and complex background, resulting in low detection accuracy, high rate of misdiagnosis and underdiagnosis, and challenges in meeting clinical needs. Therefore, this paper proposes the CMS-YOLO network model for multi-category brain tumor detection, which is based on the You Only Look Once version 10 (YOLOv10s) algorithm. This model… More >

Yu-Shiuan Tsai^*, Yuk-Hang Sit

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3065-3090, 2025, DOI:10.32604/cmes.2025.064783 - 30 June 2025

Abstract To improve small object detection and trajectory estimation from an aerial moving perspective, we propose the Aerial View Attention-PRB (AVA-PRB) model. AVA-PRB integrates two attention mechanisms—Coordinate Attention (CA) and the Convolutional Block Attention Module (CBAM)—to enhance detection accuracy. Additionally, Shape-IoU is employed as the loss function to refine localization precision. Our model further incorporates an adaptive feature fusion mechanism, which optimizes multi-scale object representation, ensuring robust tracking in complex aerial environments. We evaluate the performance of AVA-PRB on two benchmark datasets: Aerial Person Detection and VisDrone2019-Det. The model achieves 60.9% mAP@0.5 on the Aerial Person… More >

Keke Zhou, Guoqiang Zheng^*, Huihui Zhai, Xiangshuai Lv, Weizhen Zhang

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2571-2585, 2024, DOI:10.32604/cmc.2024.056863 - 18 November 2024

Abstract Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies. Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents, thereby providing a guarantee for the safety of drivers. However, detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds, varying target scales, and different resolutions. Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios, this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5. The algorithm integrates Attention-based Intra-scale Feature… More >