He Gong^1,3,4, Mingwang Deng¹, Shijun Li^1,2,6,*, Tianli Hu^1,3,4, Yu Sun^1,3,4, Ye Mu^1,3,4, Zilian Wang¹, Chang Zhang¹, Thobela Louis Tyasi⁵

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4953-4969, 2022, DOI:10.32604/cmc.2022.027457

Abstract With the increasing intensive and large-scale development of the sika deer breeding industry, it is crucial to assess the health status of the sika deer by monitoring their behaviours. A machine vision–based method for the behaviour recognition of sika deer is proposed in this paper. Google Inception Net (GoogLeNet) is used to optimise the model in this paper. First, the number of layers and size of the model were reduced. Then, the 5 × 5 convolution was changed to two 3 × 3 convolutions, which reduced the parameters and increased the nonlinearity of the model. A 5 × 5 convolution… More >

Xinheng Yuan, Hao Yu, Houlin Zhang, Li Zheng, Erbao Dong^*, Heng’an Wu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 281-301, 2022, DOI:10.32604/cmes.2022.021383

Abstract Robotic grasping is an essential problem at both the household and industrial levels, and unstructured objects have always been difficult for grippers. Parallel-plate grippers and algorithms, focusing on partial information of objects, are one of the widely used approaches. However, most works predict single-size grasp rectangles for fixed cameras and gripper sizes. In this paper, a multi-scale grasp detector is proposed to predict grasp rectangles with different sizes on RGB-D or RGB images in real-time for hand-eye cameras and various parallel-plate grippers. The detector extracts feature maps of multiple scales and conducts predictions on each scale independently. To guarantee independence… More >

Helong Yu, Xianhe Cheng, Ziqing Li, Qi Cai, Chunguang Bi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 711-738, 2022, DOI:10.32604/cmes.2022.020263

Abstract To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks, a lightweight ResNet (LW-ResNet) model for apple disease recognition is proposed. Based on the deep residual network (ResNet18), the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features. By improving the identity mapping structure to reduce information loss. By introducing the efficient channel attention module (ECANet) to suppress noise from a complex background. The experimental results show that the average… More >

Yunbo Rao^1,2, Hongyu Mu¹, Zeyu Yang¹, Weibin Zheng¹, Faxin Wang¹, Jiansu Pu¹, Shaoning Zeng²

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 1039-1054, 2022, DOI:10.32604/cmes.2022.020331

Abstract Multi-scale object detection is a research hotspot, and it has critical applications in many secure systems. Although the object detection algorithms have constantly been progressing recently, how to perform highly accurate and reliable multi-class object detection is still a challenging task due to the influence of many factors, such as the deformation and occlusion of the object in the actual scene. The more interference factors, the more complicated the semantic information, so we need a deeper network to extract deep information. However, deep neural networks often suffer from network degradation. To prevent the occurrence of degradation on deep neural networks,… More >

Jun Li¹, Xiang Li¹, Yifei Wei^1,*, Mei Song¹, Xiaojun Wang²

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2529-2540, 2022, DOI:10.32604/cmc.2022.029542

Abstract Image keypoint detection and description is a popular method to find pixel-level connections between images, which is a basic and critical step in many computer vision tasks. The existing methods are far from optimal in terms of keypoint positioning accuracy and generation of robust and discriminative descriptors. This paper proposes a new end-to-end self-supervised training deep learning network. The network uses a backbone feature encoder to extract multi-level feature maps, then performs joint image keypoint detection and description in a forward pass. On the one hand, in order to enhance the localization accuracy of keypoints and restore the local shape… More >

Adel Hafeezallah¹, Ahlam Al-Dhamari^2,3,*, Syed Abd Rahman Abu-Bakar²

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3879-3903, 2022, DOI:10.32604/cmc.2022.028289

Abstract Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large gathering. Moreover, since the crowd images in this case can range from low density to high density, detection-based approaches are hard to apply for crowd counting. Recently, deep learning-based regression has become the prominent approach for crowd counting problems, where a density-map is estimated, and its integral is further computed to acquire the final count result. In this paper, we put forward a novel multi-scale network (named 2U-Net)… More >

Jie Chen¹, Chengsheng Yuan^1,2,*, Chen Cui², Zhihua Xia¹, Xingming Sun^1,3, Thangarajah Akilan⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 719-733, 2022, DOI:10.32604/cmc.2022.027984

Abstract Fingerprint identification systems have been widely deployed in many occasions of our daily life. However, together with many advantages, they are still vulnerable to the presentation attack (PA) by some counterfeit fingerprints. To address challenges from PA, fingerprint liveness detection (FLD) technology has been proposed and gradually attracted people's attention. The vast majority of the FLD methods directly employ convolutional neural network (CNN), and rarely pay attention to the problem of over-parameterization and over-fitting of models, resulting in large calculation force of model deployment and poor model generalization. Aiming at filling this gap, this paper designs a lightweight multi-scale convolutional… More >

J. Rajalakshmi^*, S. Siva Ranjani

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1049-1063, 2022, DOI:10.32604/iasc.2022.025127

Abstract Recently many algorithms for allocation of power approaches have been suggested to increase the Energy Efficiency (EE) and Spectral Efficiency (EE) in the Distributed Antenna System (DAS). In addition, the method of conservation developed for the allocation of power is challenging for the enhancement because of their high complication during estimation. With the intention of increasing the EE and SE, the optimization of allocation of power is done on the basis of capacity of the antenna. The main goal is for the optimization of the power allocation to improve the spectral and energy efficiency with the increased capacity of the… More >

U. Hari, A. Ruhan Bevi^*

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 957-967, 2022, DOI:10.32604/iasc.2022.023605

Abstract Removal of noise from images is very important as a clear, denoised image is essential for any application. In this article, a modified haze removal algorithm is developed by applying combined dark channel prior and multi-scale retinex theory. The combined dark channel prior (DCP) and bright channel prior (BCP) together with the multi-scale retinex (MSR) algorithm is used to dynamically optimize the transmission map and thereby improve visibility. The proposed algorithm performs effective denoising of images considering the properties of retinex theory. The proposed method removes haze on an image scene through estimation of the atmospheric light and manipulating the… More >

Yin Liang^1,*, Gaoxu Xu¹, Sadaqat ur Rehman²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4645-4661, 2022, DOI:10.32604/cmc.2022.026999

Abstract Whole brain functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in the diagnosis of brain disorders such as autism spectrum disorder (ASD). Recently, an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification. However, the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification. In this paper, we proposed a multi-scale attention-based deep neural network (MSA-DNN) model to classify FC patterns for the ASD diagnosis.… More >