Qunyue Mu^1,2, Qiancheng Yu^1,2,*, Chengchen Zhou^1,2, Lei Liu^1,2, Xulong Yu^1,2

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 449-466, 2024, DOI:10.32604/cmc.2024.051728 - 18 July 2024

Abstract Wearing helmets while riding electric bicycles can significantly reduce head injuries resulting from traffic accidents. To effectively monitor compliance, the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles. However, manual enforcement by traffic police is time-consuming and labor-intensive. Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques. This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles, addressing these challenges. The More >

Yakui Liu^1,2,3,*, Xing Jiang¹, Ruikang Xu¹, Yihao Cui¹, Chenhui Yu¹, Jingqi Yang¹, Jishuai Zhou¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1263-1279, 2024, DOI:10.32604/cmc.2024.048864 - 25 April 2024

Abstract The rapid pace of urban development has resulted in the widespread presence of construction equipment and increasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safe operation of the power grid. Machine vision technology, particularly object recognition technology, has been widely employed to identify foreign objects in transmission line images. Despite its wide application, the technique faces limitations due to the complex environmental background and other auxiliary factors. To address these challenges, this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replaced with a spatial-depth… More >