Xiaorui Zhang^1,2,3,4,*, Ziquan Zhao¹, Wei Sun^4,5, Qi Cui⁶

Computer Systems Science and Engineering, Vol.46, No.1, pp. 537-550, 2023, DOI:10.32604/csse.2023.034230

Abstract LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box (BBox). However, under the three-dimensional space of autonomous driving scenes, the previous object detection methods, due to the pre-processing of the original LIDAR point cloud into voxels or pillars, lose the coordinate information of the original point cloud, slow detection speed, and gain inaccurate bounding box positioning. To address the issues above, this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++, which effectively preserves the original point cloud coordinate… More >

Chuanlong Sun, Hong Zhao^*, Liang Mu, Fuliang Xu, Laiwei Lu

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 787-801, 2023, DOI:10.32604/cmes.2023.025119

Abstract Image semantic segmentation has become an essential part of autonomous driving. To further improve the generalization ability and the robustness of semantic segmentation algorithms, a lightweight algorithm network based on Squeeze-and-Excitation Attention Mechanism (SE) and Depthwise Separable Convolution (DSC) is designed. Meanwhile, Adam-GC, an Adam optimization algorithm based on Gradient Compression (GC), is proposed to improve the training speed, segmentation accuracy, generalization ability and stability of the algorithm network. To verify and compare the effectiveness of the algorithm network proposed in this paper, the trained network model is used for experimental verification and comparative test More >

Jianwei Yuan¹, Xinli Song^1,*, Huaijian Pu², Zhixiong Zheng³, Ziyang Niu³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6485-6503, 2023, DOI:10.32604/cmc.2023.035165

Abstract Regular inspection of bridge cracks is crucial to bridge maintenance and repair. The traditional manual crack detection methods are time-consuming, dangerous and subjective. At the same time, for the existing mainstream vision-based automatic crack detection algorithms, it is challenging to detect fine cracks and balance the detection accuracy and speed. Therefore, this paper proposes a new bridge crack segmentation method based on parallel attention mechanism and multi-scale features fusion on top of the DeeplabV3+ network framework. First, the improved lightweight MobileNet-v2 network and dilated separable convolution are integrated into the original DeeplabV3+ network to improve… More >

Wei Wu¹, Weigong Zhang^1,*, Dong Wang¹, Lydia Zhu², Xiang Song³

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3811-3828, 2023, DOI:10.32604/cmc.2023.029787

Abstract An increase in car ownership brings convenience to people’s life. However, it also leads to frequent traffic accidents. Precisely forecasting surrounding agents’ future trajectories could effectively decrease vehicle-vehicle and vehicle-pedestrian collisions. Long-short-term memory (LSTM) network is often used for vehicle trajectory prediction, but it has some shortages such as gradient explosion and low efficiency. A trajectory prediction method based on an improved Transformer network is proposed to forecast agents’ future trajectories in a complex traffic environment. It realizes the transformation from sequential step processing of LSTM to parallel processing of Transformer based on attention mechanism. More >

Zhiyun Yang^1,#, Qi Liu^1,#,*, Hao Wu¹, Xiaodong Liu², Yonghong Zhang³

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 45-64, 2023, DOI:10.32604/cmes.2022.022045

Abstract Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain. Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation, where radar echo maps were used to predict their consequent moment, so as to recognize potential severe convective weather events. However, these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation, due to the size limitation of convolution filter, lack of global… More > Graphic Abstract

Hyun-Sam Shin¹, Jun-Ki Hong^2,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 235-248, 2023, DOI:10.32604/iasc.2023.033082

Abstract Recently, artificial-intelligence-based automatic customer response system has been widely used instead of customer service representatives. Therefore, it is important for automatic customer service to promptly recognize emotions in a customer’s voice to provide the appropriate service accordingly. Therefore, we analyzed the performance of the emotion recognition (ER) accuracy as a function of the simulation time using the proposed chunk-based speech ER (CSER) model. The proposed CSER model divides voice signals into 3-s long chunks to efficiently recognize characteristically inherent emotions in the customer’s voice. We evaluated the performance of the ER of voice signal chunks More >

Yongjie Yang¹, Shanshan Tu¹, Raja Hashim Ali², Hisham Alasmary^3,4, Muhammad Waqas^5,6,*, Muhammad Nouman Amjad⁷

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 801-815, 2023, DOI:10.32604/cmc.2023.031907

Abstract With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect… More >

Xiliang Zhang¹, Jin Liu^1,*, Yue Li¹, Zhongdai Wu^2,3, Y. Ken Wang⁴

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6407-6424, 2022, DOI:10.32604/cmc.2022.027097

Abstract Performance of Video Question and Answer (VQA) systems relies on capturing key information of both visual images and natural language in the context to generate relevant questions’ answers. However, traditional linear combinations of multimodal features focus only on shallow feature interactions, fall far short of the need of deep feature fusion. Attention mechanisms were used to perform deep fusion, but most of them can only process weight assignment of single-modal information, leading to attention imbalance for different modalities. To address above problems, we propose a novel VQA model based on Triple Multimodal feature Cyclic Fusion More >

S. Prabu, K. Joseph Abraham Sundar^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2071-2086, 2023, DOI:10.32604/iasc.2023.029105

Abstract Recognizing irregular text in natural images is a challenging task in computer vision. The existing approaches still face difficulties in recognizing irregular text because of its diverse shapes. In this paper, we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder architecture. The proposed framework is divided into four main modules. Firstly, in the image transformation module, a Thin Plate Spline (TPS) transformation is employed to transform the irregular text image into a readable text image. Secondly, we propose a novel Spatial Attention Module (SAM) to compel the model to concentrate… More >

Huiping Jiang^1,*, Demeng Wu¹, Xingqun Tang¹, Zhongjie Li¹, Wenbo Wu²

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2697-2712, 2022, DOI:10.32604/cmc.2022.027856

Abstract Emotions serve various functions. The traditional emotion recognition methods are based primarily on readily accessible facial expressions, gestures, and voice signals. However, it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications. Electroencephalogram (EEG) signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage. Although EEG signals are commonly used in current emotional recognition research, the accuracy is low when using traditional methods. Therefore, this study presented an optimized hybrid pattern with an attention mechanism (FFT_CLA) for… More >