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Crack Width Recognition of Tunnel Tube Sheet Based on yolov8 algorithm and 3D Imaging

BUILDINGS 2024年第2期14卷 531页

作者： Xu, Xunqian Li, Qi Li, Shue Kang, Fengyi Wan, Guozhi Wu, Tao Wang, Siwen Nantong Univ Sch Transportat & Civil Engn Nantong 226019 Peoples R China Nantong Highway Dev Ctr Nantong 226007 Peoples R China

Based on the tunnel crack width identification, there are operating time constraints, limited operating space, high equipment testing costs, and other issues. In this paper, a large subway tunnel is a research object, and the tunnel rail inspection car is an operating platform equipped with industrial cameras in order to meet the requirements of the tunnel tube sheet crack width recognition of more than 0.2 mm, with the measuring instrument to verify that the tunnel rail inspection car in the state of uniform motion camera imaging quality has the reliability through the addition of laser rangefinders, the accurate measurement of the object distance and the calculation of the imaging plane and the angle of the plane to be measured, to amend the three-dimensional cracks. The pixel resolution of the image is corrected, the images imaged by the industrial camera are preprocessed, the yolov8 algorithm is used for the intelligent extraction of crack morphology, and finally, the actual width is calculated from the spacing between two points of the crack. The crack detection width obtained by image processing using the yolov8 algorithm is basically the same as the value of crack width obtained by manual detection, and the error rate of crack width detection ranges from 0% to 11%, with the average error rate remaining below 4%. Compared with the crack detection error rate of the Support Vector Machine (SVM), the crack extraction model is reduced by 1%, so using the tunnel inspection vehicle as a platform equipped with an industrial camera, yolov8 is used to realize the recognition of the shape and width of the cracks on the surface of the tunnel tube sheet to meet the requirements of a higher degree of accuracy. The number of pixels and the detection error rate are inversely proportional to each other. The angle between the imaging plane and the plane under test is directly proportional to the detection error rate. The angle between the vertical axis where the lens midpoint

关键词： tunnel tube sheet laser range finder image processing yolov8 algorithm crack width

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A defect detection method for industrial aluminum sheet surface based on improved yolov8 algorithm

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FRONTIERS IN PHYSICS 2024年 12卷

作者： Wang, Luyang Zhang, Gongxue Wang, Weijun Chen, Jinyuan Jiang, Xuyao Yuan, Hai Huang, Zucheng Shaanxi Univ Sci & Technol Coll Mech & Elect Engn Xian Peoples R China Guangzhou Inst Adv Technol Guangzhou Peoples R China

In industrial aluminum sheet surface defect detection, false detection, missed detection, and low efficiency are prevalent challenges. Therefore, this paper introduces an improved yolov8 algorithm to address these issues. Specifically, the C2f-DSConv module incorporated enhances the network's feature extraction capabilities, and a small target detection layer (160 x 160) improves the recognition of small targets. Besides, the DyHead dynamic detection head augments target representation, and MPDIoU replaces the regression loss function to refine detection accuracy. The improved algorithm is named yolov8n-DSDM, with experimental evaluations on an industrial aluminum sheet surface defect dataset demonstrating its effectiveness. yolov8n-DSDM achieves an average mean average precision (mAP50%) of 94.7%, demonstrating a 3.5% improvement over the original yolov8n. With a single-frame detection time of 2.5 ms and a parameter count of 3.77 M, yolov8n-DSDM meets the real-time detection requirements for industrial applications.

关键词： defect detection yolov8 algorithm C2f-DSConv module DyHead dynamic detection head network small target detection layer

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Small Object Detection with yolov8 algorithm Enhanced by MobileViTv3 and Wise-IoU 23

Small Object Detection with YOLOv8 Algorithm Enhanced by Mob...

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Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition

作者： Wencheng Wang Wenxin Liu Shandong Agricultural University China

ISBN: (纸本)9798400707988

Small object detection is an important and challenging task in computer vision, with widespread applications in fields such as remote sensing, autonomous driving, and security. However, due to factors such as small size, indistinct features, and complex backgrounds of small objects, traditional convolutional neural network (CNN)-based object detection algorithms often struggle to effectively detect small objects. To address issues such as insufficient feature extraction, suboptimal anchor box matching, and unbalanced loss functions, an improved yolov8 algorithm is proposed. This algorithm enhances the detection accuracy of small objects by adding a lightweight visual transformer, MobileViTv3, which can enhance feature representation capabilities, and a Wise-IoU loss function that adaptively adjusts the ratio of positive and negative samples and regression coefficients. Results show that the improved yolov8 algorithm achieves an increase of 2.5%, 2.1%, and 2.9% in precision, recall, and mAP respectively on the public remote sensing dataset VisDrone2019 and 2.4%, 2.7%, and 3.5% on DOTA compared to the original algorithm. It can effectively improve the accuracy of small object detection in complex scenarios while meeting the requirements for detection speed.

关键词： MobileViTv3 Small object detection Wise-IoU yolov8 algorithm

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BFL-yolov8: surface defect detection algorithm for titanium alloy plates based on improved yolov8

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Insight: Non-Destructive Testing and Condition Monitoring 2025年第4期67卷 208-214页

作者： Luo, ShuYuan Zhao, Jun Jia, Bin Niu, QingSong School of Materials Science and Engineering Xiamen University of Technology Xiamen361024 China

Defect detection in titanium alloy plates is a crucial step in industrial production, but surface defects are often too minute and subject to lighting, leading to missed detections in industrial environments. To address this issue, an improved defect detection algorithm, BFL-yolov8, based on the you only look once v8 (yolov8) model, is proposed to detect common surface defects on titanium alloy plates, such as cracks, spots and scratches. The system incorporates the BiFormer attention mechanism to enhance the ability of the model to extract and process image features, integrates the bidirectional feature pyramid network (BiFPN) for weighted fusion and bidirectional cross-scale connections and further uses the regularisation flow technique of the LLFlow algorithm to eliminate the interference of highlights and shadows in the dataset. The experimental results show that the BFL-yolov8 achieves a mean average precision (mAP) of 93.8% on the titanium alloy plate defect dataset, an 8.6% improvement over the original yolov8 model, and balances detection accuracy and speed well. It demonstrates excellent detection abilities compared to other similar target detection models and can be applied to defect detection tasks for titanium alloy plates in various complex environments. © 2025 British Institute of Non-Destructive Testing. All rights reserved.

关键词： defect detection industrial automation metal materials self-attentive mechanism yolov8 algorithm

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Traffic Sign Detection and Quality Assessment Using yolov8 in Daytime and Nighttime Conditions

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SENSORS 2025年第4期25卷 1027-1027页

作者： Aldoski, Ziyad N. Koren, Csaba Duhok Polytech Univ Tech Coll Engn Dept Highway & Bridge Duhok 1006 Kurdistan Regio Iraq Szecheny Istvan Univ Fac Architecture Civil Engn & Transportat Sci Dept Transport Infrastruct & Water Resources Engn H-9026 Gyor Hungary

Traffic safety remains a pressing global concern, with traffic signs playing a vital role in regulating and guiding drivers. However, environmental factors like lighting and weather often compromise their visibility, impacting human drivers and autonomous vehicle (AV) systems. This study addresses critical traffic sign detection (TSD) and classification (TSC) gaps by leveraging the yolov8 algorithm to evaluate the detection accuracy and sign quality under diverse lighting conditions. The model achieved robust performance metrics across day and night scenarios using the novel ZND dataset, comprising 16,500 labeled images sourced from the GTSRB, GitHub repositories, and real-world own photographs. Complementary retroreflectivity assessments using handheld retroreflectometers revealed correlations between the material properties of the signs and their detection performance, emphasizing the importance of the retroreflective quality, especially under night-time conditions. Additionally, video analysis highlighted the influence of sharpness, brightness, and contrast on detection rates. Human evaluations further provided insights into subjective perceptions of visibility and their relationship with algorithmic detection, underscoring areas for potential improvement. The findings emphasize the need for using various assessment methods, advanced algorithms, enhanced sign materials, and regular maintenance to improve detection reliability and road safety. This research bridges the theoretical and practical aspects of TSD, offering recommendations that could advance AV systems and inform future traffic sign design and evaluation standards.

关键词： traffic safety traffic sign detection yolov8 algorithm retroreflectivity autonomous vehicles camera sensor human evaluation

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Bolt Loosening Detection Method Based on Improved yolov8 and Image Matching

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IEEE ACCESS 2025年 13卷 1133-1146页

作者： Jiang, Peihe Geng, Yuhang Sang, Zhongqi Lin, Lifeng Yantai Univ Sch Phys & Elect Informat Yantai 264005 Peoples R China Peking Univ Sch Elect Engn Beijing 100091 Peoples R China Shandong Huali Electromech Co Ltd Jining 272000 Peoples R China

Bolt connections are widely used as structural connections in civil engineering, mechanical engineering, and bridge construction. However, problems such as loosening, or breakage can occur with bolts after prolonged use. To address the challenges of detecting bolt loosening, this study reviews existing detection technologies, analyzes their advantages and limitations, and proposes a novel bolt-loosening detection algorithm based on image matching and deep learning. The algorithm comprises the following components: a bolt target detection model based on an improved yolov8 algorithm, image correction using perspective transformation, bolt contour detection and image processing, and feature matching to calculate the transformation matrix between images obtained before and after loosening, thereby determining the loosening angle of the bolt. The experiments focused on a rectangular steel plate featuring four M6 standard bolts. The results demonstrate that the bolt target detection model can accurately locate and crop bolt positions and identify loosening angles under various shooting angles, distances, and lighting conditions. At specific shooting angles and appropriate distances, the detection error threshold was less than 2 degrees. Subsequently, experiments conducted in real-world scenarios confirmed the accuracy and feasibility of the proposed algorithm.

关键词： Fasteners Feature extraction Accuracy YOLO Deep learning Image processing Image matching Lighting Distortion Monitoring Bolt loosening detection yolov8 algorithm feature matching contour detection

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Typical Surface Defects Detection of Concrete Dam Using yolov8+U-Net

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INTERNATIONAL JOURNAL OF CIVIL ENGINEERING 2025年 1-16页

作者： Huang, Yaoying Liu, Hui Xu, Shimei Wei, Haidong Hameed, Aamir China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Peoples R China Guangdong Yuedian Nanshui Power Generat Co Ltd Shaoguan Peoples R China

Manual detection of concrete dam defects is inefficient and risky, and traditional digital image processing techniques often suffer from poor accuracy, leading to frequent misdetections. In addition, the existing "one-step" networks are inadequate for multicategory detection of concrete defects and are too slow and inaccurate for defect identification. To address these issues, this work studies deep-learning-based image detection for typical defects of concrete dams. First, typical defect images of concrete dams are analyzed, and a typical defect dataset of concrete dams is constructed by simulating the defects of self-fabricated concrete slabs. The defect detection model of concrete dams is established using the yolov8 + U-Net algorithm. Finally, defect images of a concrete dam acquired on-site are used as test cases, and the established detection model is applied for intelligent detection. The results show that the "two-step method" based on the yolov8 + U-Net algorithm can realize efficient and accurate detection of water leakage and cracks in concrete dams. The accuracy rate of recognition and location of the model is 0.84, the recall rate is 0.98, the segmentation accuracy rate is 0.91, and the recall rate is 0.74. The established defect detection model was applied to detect defects in an actual gravity dam, which shows the "two-step method" model produces good results for classifying and locating typical defects in the segmentation model targets.

关键词： Deep learning Concrete dam Image detection yolov8 algorithm U-Net algorithm

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Anti-interference Pine Wilt Disease Detection algorithm Based on Multilevel Residual Fusion in yolov8 8

Anti-interference Pine Wilt Disease Detection Algorithm Base...

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8th International Conference on Smart Internet of Things

作者： Wang, Lina Yuan, Junchao Cai, Jijing Deng, Jiangtao Yang, Zijia Wen, Zhihao Fang, Kai Feng, Hailin Zhejiang A&F Univ Hangzhou Peoples R China Hangzhou Dianzi Univ Hangzhou Peoples R China

ISBN: (纸本)9798350366457;9798350366440

Pine Wilt Disease (PWD) can severely threaten the health of pine trees. Monitoring pine forests with drones can effectively prevent the spread of PWD. Existing object detection models maintain high recognition accuracy under normal conditions, but their accuracy significantly decreases when there are conditions such as cloud cover and atmospheric scattering. Therefore, this paper proposes an anti-interference pine wilt disease detection algorithm based on multilevel residual fusion in yolov8, named yolov8-MD. We designed a Multi-level Residual module (MRes), which can filter out image noise. The MRes module not only effectively enhances the detail features of small targets in images but also filters out image noise through the structure of multi-level residual blocks. Additionally, we integrated a Dynamic Upsampling algorithm (DySample) into the Neck. DySample module improves the quality of feature maps during the upsampling process by adaptively adjusting the sampling points. We simulated the situation of cloud cover using Gaussian noise. Experimental results show that, at an interference ratio of 100% and an interference intensity of 7, the average precision of the yolov8-MD model improved by 6.5%, 8.2%, and 7.6% compared to three traditional object detection models.

关键词： yolov8 algorithm Interference Environment Multi-level Residual Dynamic Upsampling

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A Fast Detection algorithm for Surface Defects of Bare PCB Based on yolov8 3

A Fast Detection Algorithm for Surface Defects of Bare PCB B...

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3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Yan, Manle Fan, Yong Jiang, Yong Fang, Zhaoyan School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350359961

Bare PCB (Printed Circuit Board) surface defect detection is an important step in the automated production of PCB. To meet the requirements of production efficiency, the detection must have high real-time performance, therefore we propose the MOP-Block module to replace the C2f module and also improve the yolov8 network structure to reduce the inference time. Then, considering that most of the surface defects on the bare PCB are small in size compared with the overall product, we propose the SiLU-SE attention module to enhance the feature map for improving the detection accuracy at a lower computational cost. The experimental results show that compared with the original yolov8n, our algorithms improves the FPS (batch=1, without TensorRT acceleration) on the Tesla T4 GPU by about 70.17%, the size of model is reduced by about 77.01 %, the parameters of the model are reduced by about 84%, and the FLOPs of the model are reduced about 53.08%, but map@0.5 and map@0.5:0.95 are only reduced by about 2.6(%) and 0.9(%) respectively. © 2023 IEEE.

关键词： Lightweight PCB bare board surface defect detection Reparameterization SE Attention yolov8 algorithm

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Small Target Detection algorithm of Edge Scene Based on Improved yolov8 13

Small Target Detection Algorithm of Edge Scene Based on Impr...

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13th International Conference of Information and Communication Technology, ICTech 2024

作者： Wang, Hanjun Ma, Ziqi Shenyang Institute of Computing Technologies CAS University of Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350376258

Presents an enhanced small target detection algorithm for edge scene applications based on the improved yolov8 framework, termed yolov8-VCS. The primary focus is on the detection and identification of small objects within edge scenarios, characterized by environments with limited computing resources, such as smart cameras and IoT devices. Recognizing the significance of efficient small target detection in these contexts, the study introduces modifications to the yolov8 algorithm to optimize performance in edge computing environments. The proposed yolov8-VCS algorithm incorporates three main improvements: the Vanillanet module for model compression, enabling faster and more real-time detection;the Coordinate Attention (CA) module to enhance feature extraction and improve detection accuracy;and the Slicing Aided Hyper Inference (SAHI) algorithm to augment the recognition of small objects, thereby boosting detection precision. These enhancements aim to balance the trade-off between detection speed and accuracy, achieving real-time performance with reduced model complexity. Experimental results, conducted using the Tsinghua-Tencent 100K (TT100K) dataset, demonstrate the effectiveness of the proposed algorithm, showing significant improvements in detection accuracy and speed while minimizing model parameters and computational demands. The yolov8-VCS algorithm outperforms existing models by achieving higher detection accuracy with fewer parameters and lower computation, making it well-suited for deployment in resource-constrained edge devices. ©2024 IEEE.

关键词： boundary detection small target detection yolov8 algorithm

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