Zhijian Liu^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3787-3803, 2024, DOI:10.32604/cmc.2024.048998

Abstract Considering the variations in imaging sizes of the unmanned aerial vehicles (UAV) at different aerial photography heights, as well as the influence of factors such as light and weather, which can result in missed detection and false detection of the model, this paper presents a comprehensive detection model based on the improved lightweight You Only Look Once version 8s (YOLOv8s) algorithm used in natural light and infrared scenes (L_YOLO). The algorithm proposes a special feature pyramid network (SFPN) structure and substitutes most of the neck feature extraction module with the Special deformable convolution feature extraction module (SDCN). Moreover, the model… More >

Chun-Ming Wu¹, Jin Lei^1,*, Wu-Kai Liu¹, Mei-Ling Ren¹, Ling-Li Ran²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3071-3088, 2024, DOI:10.32604/cmc.2023.047062

Abstract Aiming at defects such as low contrast in infrared ship images, uneven distribution of ship size, and lack of texture details, which will lead to unmanned ship leakage misdetection and slow detection, this paper proposes an infrared ship detection model based on the improved YOLOv8 algorithm (R_YOLO). The algorithm incorporates the Efficient Multi-Scale Attention mechanism (EMA), the efficient Reparameterized Generalized-feature extraction module (CSPStage), the small target detection header, the Repulsion Loss function, and the context aggregation block (CABlock), which are designed to improve the model’s ability to detect targets at multiple scales and the speed of model inference. The algorithm… More >

Weiya Shi^1,*, Shaowen Zhang², Shiqiang Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3209-3231, 2024, DOI:10.32604/cmes.2023.044863

Abstract In recent years, there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks. Despite these efforts, the detection of small objects in remote sensing remains a formidable challenge. The deep network structure will bring about the loss of object features, resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers. Additionally, the features of small objects are susceptible to interference from background features contained within the image, leading to a decline in detection accuracy. Moreover, the sensitivity of small… More >

Xiao Luo^1,3, Hao Zhu^1,2,*, Zhenli Zhang^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2667-2687, 2024, DOI:10.32604/cmc.2024.047988

Abstract Road traffic safety can decrease when drivers drive in a low-visibility environment. The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents. To tackle the challenges posed by the low recognition accuracy and the substantial computational burden associated with current infrared pedestrian-vehicle detection methods, an infrared pedestrian-vehicle detection method A proposal is presented, based on an enhanced version of You Only Look Once version 5 (YOLOv5). First, A head specifically designed for detecting small targets has been integrated into the model to make full… More >

Liqiu Ren, Zhanying Li^*, Xueyu He, Lingyan Kong, Yinghao Zhang

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2829-2845, 2024, DOI:10.32604/cmc.2024.047028

Abstract For underwater robots in the process of performing target detection tasks, the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model, which is prone to issues like error detection, omission detection, and poor accuracy. Therefore, this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7) underwater target detection algorithm. To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase, we have added a Convolutional Block Attention Module (CBAM) to the backbone network. The Reparameterization Visual Geometry Group (RepVGG) module is inserted into the… More >

Yongliang Yang, Linghua Xu^*, Maolin Luo, Xiao Wang, Min Cao

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2741-2765, 2024, DOI:10.32604/cmc.2024.046768

Abstract Due to the complex environment of the university laboratory, personnel flow intensive, personnel irregular behavior is easy to cause security risks. Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed. Therefore, the current management of personnel behavior mainly relies on institutional constraints, education and training, on-site supervision, etc., which is time-consuming and ineffective. Given the above situation, this paper proposes an improved You Only Look Once version 7 (YOLOv7) to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy. First, to better capture the shape features of the target,… More >

Yuecheng Yu^1,*, Liming Cai¹, Anqi Ning¹, Jinlong Shi¹, Xudong Chen², Shixin Huang¹

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1915-1931, 2024, DOI:10.32604/cmc.2024.046125

Abstract Under the influence of air humidity, dust, aerosols, etc., in real scenes, haze presents an uneven state. In this way, the image quality and contrast will decrease. In this case, It is difficult to detect the target in the image by the universal detection network. Thus, a dual subnet based on multi-task collaborative training (DSMCT) is proposed in this paper. Firstly, in the training phase, the Gated Context Aggregation Network (GCANet) is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes. In the test phase, only the YOLOX branch needs to be… More >

Yaoyao Du, Xiangkui Jiang^*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 303-327, 2024, DOI:10.32604/cmc.2023.046068

Abstract To address the challenges of high complexity, poor real-time performance, and low detection rates for small target vehicles in existing vehicle object detection algorithms, this paper proposes a real-time lightweight architecture based on You Only Look Once (YOLO) v5m. Firstly, a lightweight upsampling operator called Content-Aware Reassembly of Features (CARAFE) is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles, reducing the missed detection rate and false detection rate. Secondly, a new prediction layer for tiny targets is added, and the feature fusion network is redesigned to enhance the… More >

Hyun Kwon^1,*, Sanghyun Lee²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 249-263, 2024, DOI:10.32604/cmc.2023.042466

Abstract Deep neural networks perform well in image recognition, object recognition, pattern analysis, and speech recognition. In military applications, deep neural networks can detect equipment and recognize objects. In military equipment, it is necessary to detect and recognize rifle management, which is an important piece of equipment, using deep neural networks. There have been no previous studies on the detection of real rifle numbers using real rifle image datasets. In this study, we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient. The proposed method was designed to improve the recognition rate of a specific… More >

Jinhai Wang¹, Wei Wang¹, Zongyin Zhang¹, Xuemin Lin¹, Jingxian Zhao¹, Mingyou Chen¹, Lufeng Luo^2,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 759-780, 2024, DOI:10.32604/cmc.2023.041600

Abstract As computer technology continues to advance, factories have increasingly higher demands for detecting defects. However, detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes. To address this issue, this paper proposes YOLO-DD, a defect detection model based on YOLOv5 that is effective and robust. To improve the feature extraction process and better capture global information, the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer (RDAT). Additionally, an Information Gap Filling Strategy (IGFS) is proposed to improve the fusion of features at different… More >