Conghao Niu^1,*, Dezhi Han¹, Bing Han², Zhongdai Wu²

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1723-1748, 2024, DOI:10.32604/csse.2024.056736 - 22 November 2024

Abstract The high coverage and all-weather capabilities of Synthetic Aperture Radar (SAR) image ship detection make it a widely accepted method for maritime ship positioning and identification. However, SAR ship detection faces challenges such as indistinct ship contours, low resolution, multi-scale features, noise, and complex background interference. This paper proposes a lightweight YOLOv8 model for small object detection in SAR ship images, incorporating key structures to enhance performance. The YOLOv8 backbone is replaced by the Slim Backbone (SB), and the Delete Medium-sized Detection Head (DMDH) structure is eliminated to concentrate on shallow features. Dynamically adjusting the… More >

Zongdong Du^1,2, Xuefeng Feng³, Feng Li³, Qinglong Xian³, Zhenhong Jia^1,2,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2607-2627, 2024, DOI:10.32604/cmc.2024.056616 - 18 November 2024

Abstract The importance of unmanned aerial vehicle (UAV) obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance, thereby protecting people and property. We propose UAD-YOLOv8, a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance. The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2 (DCNv2) to optimize the cross stage partial bottleneck with 2 convolutions and fusion (C2f) module. Additionally, it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable… More >

Chenghai Yu, Zhilong Lu^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3261-3280, 2024, DOI:10.32604/cmc.2024.056413 - 18 November 2024

Abstract Railway turnouts often develop defects such as chipping, cracks, and wear during use. If not detected and addressed promptly, these defects can pose significant risks to train operation safety and passenger security. Despite advances in defect detection technologies, research specifically targeting railway turnout defects remains limited. To address this gap, we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex environments. To enhance detection accuracy, we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU (YOLO-VSI). The model employs a state-space model (SSM) to enhance the C2f module in the YOLOv8… More >

Mehwash Farooqui¹, Atta Rahman^2,*, Latifa Alsuliman¹, Zainab Alsaif¹, Fatimah Albaik¹, Cadi Alshammari¹, Razan Sharaf¹, Sunday Olatunji¹, Sara Waslallah Althubaiti¹, Hina Gull³

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2587-2605, 2024, DOI:10.32604/cmc.2024.055262 - 18 November 2024

Abstract The proposed study focuses on the critical issue of corrosion, which leads to significant economic losses and safety risks worldwide. A key area of emphasis is the accuracy of corrosion detection methods. While recent studies have made progress, a common challenge is the low accuracy of existing detection models. These models often struggle to reliably identify corrosion tendencies, which are crucial for minimizing industrial risks and optimizing resource use. The proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network (CNN), as well as two pretrained… More >

Rehman Ullah Khan¹, Fazal Shah^2,*, Ahmad Ali Khan³, Hamza Tahir²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 345-367, 2024, DOI:10.32604/cmc.2024.054439 - 15 October 2024

Abstract Printed Circuit Boards (PCBs) are materials used to connect components to one another to form a working circuit. PCBs play a crucial role in modern electronics by connecting various components. The trend of integrating more components onto PCBs is becoming increasingly common, which presents significant challenges for quality control processes. Given the potential impact that even minute defects can have on signal traces, the surface inspection of PCB remains pivotal in ensuring the overall system integrity. To address the limitations associated with manual inspection, this research endeavors to automate the inspection process using the YOLOv8… More >

Shunshun Ji, Jiajun Sun, Chao Zhang^*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4017-4029, 2024, DOI:10.32604/cmc.2024.055038 - 12 September 2024

Abstract Asparagus stem blight, also known as “asparagus cancer”, is a serious plant disease with a regional distribution. The widespread occurrence of the disease has had a negative impact on the yield and quality of asparagus and has become one of the main problems threatening asparagus production. To improve the ability to accurately identify and localize phenotypic lesions of stem blight in asparagus and to enhance the accuracy of the test, a YOLOv8-CBAM detection algorithm for asparagus stem blight based on YOLOv8 was proposed. The algorithm aims to achieve rapid detection of phenotypic images of asparagus… More >

Hongjiang Wang¹, Qingze Shen^2,*, Qin Dai¹, Yingcai Gao², Jing Gao², Tian Zhang^3,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 625-642, 2024, DOI:10.32604/cmc.2024.051757 - 18 July 2024

Abstract Deep learning has emerged in many practical applications, such as image classification, fault diagnosis, and object detection. More recently, convolutional neural networks (CNNs), representative models of deep learning, have been used to solve fault detection. However, the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and error. For this reason, an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault detection. YOLOv8 is a CNN-backed object detection model. Specifically, to reduce… More >

Qunyue Mu^1,2, Qiancheng Yu^1,2,*, Chengchen Zhou^1,2, Lei Liu^1,2, Xulong Yu^1,2

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 449-466, 2024, DOI:10.32604/cmc.2024.051728 - 18 July 2024

Abstract Wearing helmets while riding electric bicycles can significantly reduce head injuries resulting from traffic accidents. To effectively monitor compliance, the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles. However, manual enforcement by traffic police is time-consuming and labor-intensive. Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques. This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles, addressing these challenges. The More >

Rui Wang¹, Zhihui Liu^2,*, Hongdi Liu³, Baozhong Su⁴, Chuanyi Ma⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3035-3049, 2024, DOI:10.32604/cmes.2024.050806 - 08 July 2024

Abstract In underground engineering, the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures. However, the dim and dusty environment inherent to underground engineering poses considerable challenges to crack segmentation. This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8 (FDSC-YOLOv8) specifically designed for underground engineering structural surfaces. Firstly, to improve the extraction of multi-layer convolutional features, the fixed convolutional module is replaced with a deformable convolutional module. Secondly, the model’s receptive field is enhanced by introducing a multi-branch More >

Zhongyuan Zhang, Wenqiu Zhu^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2547-2563, 2024, DOI:10.32604/cmc.2024.048755 - 15 May 2024

Abstract Remote sensing imagery, due to its high altitude, presents inherent challenges characterized by multiple scales, limited target areas, and intricate backgrounds. These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery. Additionally, these complexities contribute to inaccuracies in target localization and hinder precise target categorization. This paper addresses these challenges by proposing a solution: The YOLO-MFD model (YOLO-MFD: Remote Sensing Image Object Detection with Multi-scale Fusion Dynamic Head). Before presenting our method, we delve into the prevalent issues faced in remote sensing imagery… More >