Zerui Xiao, Wei Liu, Zhiwei Ye^*, Jiatang Yuan, Shishi Liu

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1009-1029, 2024, DOI:10.32604/csse.2023.040475

Abstract Nowadays, construction site safety accidents are frequent, and wearing safety helmets is essential to prevent head injuries caused by object collisions and falls. However, existing helmet detection algorithms have several drawbacks, including a complex structure with many parameters, high calculation volume, and poor detection of small helmets, making deployment on embedded or mobile devices difficult. To address these challenges, this paper proposes a YOLOv5-based multi-head detection safety helmet detection algorithm that is faster and more robust for detecting helmets on construction sites. By replacing the traditional DarkNet backbone network of YOLOv5s with a new backbone… More >

Rui Zhang^1,2, Tonghai Liu^1,2,*, Wenzheng Liu^1,2, Chaungchuang Yuan^1,2, Xiaoyue Seng^1,2, Tiantian Guo^1,2, Xue Wang^1,2

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1275-1296, 2024, DOI:10.32604/phyton.2024.052397

Abstract Rice diseases can adversely affect both the yield and quality of rice crops, leading to the increased use of pesticides and environmental pollution. Accurate detection of rice diseases in natural environments is crucial for both operational efficiency and quality assurance. Deep learning-based disease identification technologies have shown promise in automatically discerning disease types. However, effectively extracting early disease features in natural environments remains a challenging problem. To address this issue, this study proposes the YOLO-CRD method. This research selected images of common rice diseases, primarily bakanae disease, bacterial brown spot, leaf rice fever, and dry… More >

Xiaodan Wang^*, Peng Wang, Yafei Song, Qian Xiang, Jingtai Li

Computer Systems Science and Engineering, Vol.48, No.1, pp. 217-246, 2024, DOI:10.32604/csse.2023.039846

Abstract High-resolution range profile (HRRP) automatic recognition has been widely applied to military and civilian domains. Present HRRP recognition methods have difficulty extracting deep and global information about the HRRP sequence, which performs poorly in real scenes due to the ambient noise, variant targets, and limited data. Moreover, most existing methods improve the recognition performance by stacking a large number of modules, but ignore the lightweight of methods, resulting in over-parameterization and complex computational effort, which will be challenging to meet the deployment and application on edge devices. To tackle the above problems, this paper proposes… More >

Yang Yang¹, Yuhan Long¹, Yijing Lin², Zhipeng Gao¹, Lanlan Rui¹, Peng Yu^1,3,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3623-3651, 2023, DOI:10.32604/cmc.2023.040250

Abstract With the rapid development of the Internet of Things (IoT), the automation of edge-side equipment has emerged as a significant trend. The existing fault diagnosis methods have the characteristics of heavy computing and storage load, and most of them have computational redundancy, which is not suitable for deployment on edge devices with limited resources and capabilities. This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation. First, we offer a clustering-based self-knowledge distillation approach (Cluster KD), which takes the mean value of the sample diagnosis results, clusters them, and takes… More >

Yuxuan Gu, Meng Wu^*, Qian Wang, Siguang Chen, Lijun Yang

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 493-512, 2023, DOI:10.32604/cmc.2023.035974

Abstract In this paper, a deep learning-based method is proposed for crowd-counting problems. Specifically, by utilizing the convolution kernel density map, the ground truth is generated dynamically to enhance the feature-extracting ability of the generator model. Meanwhile, the “cross stage partial” module is integrated into congested scene recognition network (CSRNet) to obtain a lightweight network model. In addition, to compensate for the accuracy drop owing to the lightweight model, we take advantage of “structured knowledge transfer” to train the model in an end-to-end manner. It aims to accelerate the fitting speed and enhance the learning ability… More >

P. Anantha Prabha^1,*, G. Suchitra², R. Saravanan³

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3065-3079, 2023, DOI:10.32604/iasc.2023.030017

Abstract Cephalopods identification is a formidable task that involves hand inspection and close observation by a malacologist. Manual observation and identification take time and are always contingent on the involvement of experts. A system is proposed to alleviate this challenge that uses transfer learning techniques to classify the cephalopods automatically. In the proposed method, only the Lightweight pre-trained networks are chosen to enable IoT in the task of cephalopod recognition. First, the efficiency of the chosen models is determined by evaluating their performance and comparing the findings. Second, the models are fine-tuned by adding dense layers… More >

Mu Ye^1,2,3,4, Li Ji¹, Luo Tianye¹, Li Sihan⁵, Zhang Tong¹, Feng Ruilong¹, Hu Tianli^1,2,3,4, Gong He^1,2,3,4, Guo Ying^1,2,3,4, Sun Yu^1,2,3,4, Thobela Louis Tyasi⁶, Li Shijun^7,8,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6195-6205, 2022, DOI:10.32604/cmc.2022.026880

Abstract Remote sensing image analysis is a basic and practical research hotspot in remote sensing science. Remote sensing images contain abundant ground object information and it can be used in urban planning, agricultural monitoring, ecological services, geological exploration and other aspects. In this paper, we propose a lightweight model combining vgg-16 and u-net network. By combining two convolutional neural networks, we classify scenes of remote sensing images. While ensuring the accuracy of the model, try to reduce the memory of the model. According to the experimental results of this paper, we have improved the accuracy of… More >

Congcong Wang^{1, 2, 3}, Pengyu Liu^{1, 2, 3, *}, Kebin Jia^{1, 2, 3}, Xiaowei Jia⁴, Yaoyao Li^{1, 2, 3}

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2043-2055, 2020, DOI:10.32604/cmc.2020.010505

Abstract Weather phenomenon recognition plays an important role in the field of meteorology. Nowadays, weather radars and weathers sensor have been widely used for weather recognition. However, given the high cost in deploying and maintaining the devices, it is difficult to apply them to intensive weather phenomenon recognition. Moreover, advanced machine learning models such as Convolutional Neural Networks (CNNs) have shown a lot of promise in meteorology, but these models also require intensive computation and large memory, which make it difficult to use them in reality. In practice, lightweight models are often used to solve such More >