Zhilin Li^1,2, Yuxin Li¹, Chunyu Yan¹, Peng Yan¹, Xiutong Li¹, Mei Yu¹, Tingchi Wen^4,5, Benliang Xie^1,2,3,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 679-694, 2024, DOI:10.32604/cmc.2024.051526 - 18 July 2024

Abstract Diseases in tea trees can result in significant losses in both the quality and quantity of tea production. Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations. However, existing methods face challenges such as a high number of parameters and low recognition accuracy, which hinders their application in tea plantation monitoring equipment. This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves, to address these challenges. The proposed method first embeds a Coordinate Attention (CA) module into the original MobileNetV2 network, enabling the model to locate disease More >

Jianming Zhang^1,2,*, Kai Wang^1,2, Yaoqi He^1,2, Lidan Kuang^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 909-927, 2022, DOI:10.32604/cmes.2022.020471 - 27 June 2022

Abstract Recently, Siamese-based trackers have achieved excellent performance in object tracking. However, the high speed and deformation of objects in the movement process make tracking difficult. Therefore, we have incorporated cascaded region-proposal-network (RPN) fusion and coordinate attention into Siamese trackers. The proposed network framework consists of three parts: a feature-extraction sub-network, coordinate attention block, and cascaded RPN block.We exploit the coordinate attention block, which can embed location information into channel attention, to establish long-term spatial location dependence while maintaining channel associations. Thus, the features of different layers are enhanced by the coordinate attention block. We then More >

Soonshin Seo¹, Junseok Oh², Eunsoo Cho², Hosung Park², Gyujin Kim², Ji-Hwan Kim^2,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3291-3303, 2022, DOI:10.32604/cmc.2022.026259 - 16 June 2022

Abstract Acoustic scene classification (ASC) is a method of recognizing and classifying environments that employ acoustic signals. Various ASC approaches based on deep learning have been developed, with convolutional neural networks (CNNs) proving to be the most reliable and commonly utilized in ASC systems due to their suitability for constructing lightweight models. When using ASC systems in the real world, model complexity and device robustness are essential considerations. In this paper, we propose a two-pass mobile network for low-complexity classification of the acoustic scene, named TP-MobNet. With inverse residuals and linear bottlenecks, TP-MobNet is based on… More >