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学者姓名：陈国栋
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Abstract ：
Wearing a safety helmet and a reflective vest is essential for ensuring worker safety. While YOLO-based object detectors have demonstrated significant accuracy in detecting dress code violations, they often struggle with detecting small targets and maintaining a global focus. To address these challenges, we propose MSCG-YOLO, a novel algorithm based on YOLO networks for worker detection. Our approach integrates multi-head self-attention (MHSA) into the backbone network and neck connections, enhancing the model's global field of view and its ability to detect small and obscured targets. To further improve small target detection, we designed a new neck structure called consolidative informative systematic neck (CISNeck), which includes additional layers and an enhanced detection head. We also developed the superficial feature fusion module (SFFM) to optimize the high-resolution features of the fourth detection head. Generalized intersection over union (GIoU) was used as the loss function. Experimental results on custom datasets show that MSCG-YOLO outperforms existing methods, achieving AP and AP50 values of 52% and 91.6% on the validation set, and 53.6% and 91% on the test set. Compared to YOLOv8n, MSCG-YOLO improves AP50 scores by 3.4% on the validation set and 2.7% on the test set. In conclusion, this study effectively addresses the practical needs of dress code detection in construction scenarios.
Keyword ：
Automatic identification systems Automatic identification systems Feature fusion Feature fusion Helmet and vest detection Helmet and vest detection Occupational safety Occupational safety Small target detection Small target detection YOLO YOLO
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| GB/T 7714 | Lin, Conggong , Zhang, Yushi , Chen, Guodong . Intelligent detection of safety helmets and reflective vests based on deep learning [J]. | JOURNAL OF REAL-TIME IMAGE PROCESSING , 2025 , 22 (1) . |
| MLA | Lin, Conggong 等. "Intelligent detection of safety helmets and reflective vests based on deep learning" . | JOURNAL OF REAL-TIME IMAGE PROCESSING 22 . 1 (2025) . |
| APA | Lin, Conggong , Zhang, Yushi , Chen, Guodong . Intelligent detection of safety helmets and reflective vests based on deep learning . | JOURNAL OF REAL-TIME IMAGE PROCESSING , 2025 , 22 (1) . |
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Ensuring correct helmet usage is vital for mitigating head injuries in construction environments. Although YOLO-based detectors are proficient in dress code recognition, their performance is often constrained when detecting small objects. We introduce ADSP-YOLO, an advanced algorithm derived from YOLOv8n and specifically optimized for helmet detection. Central to this design is the adaptive boundary-semantic aggregation module, which replaces the traditional feature pyramid network and incorporates a specialized detection head tailored for small targets. To further enhance detection efficacy, we propose the scale sequence feature fusion, leveraging edge information from the P2 feature layer, and integrate the dynamic head with attention modules to refine accuracy. Model optimization is achieved through the layer-adaptive magnitude-based pruning method, enabling a balance between compression and performance. Evaluations on the safety helmet wearing dataset demonstrate that ADSP-YOLO achieves a mAP@0.5 92.7% on the test set, surpassing YOLOv8n by 2.2% and 4.2% in mAP@0.5 and APs, respectively, while reducing model parameters to 39.3% and model size to 45.8% of the original. Moreover, ADSP-YOLO achieves a mAP@0.5 of 82.3% on the GDUT-HWD dataset and demonstrates robust adaptability on the remote sensing object detection dataset, highlighting its potential for broader applications in small object detection.
Keyword ：
feature fusion feature fusion helmet detection helmet detection model pruning model pruning small target detection small target detection YOLO YOLO
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| GB/T 7714 | Zhang, Yushi , Lin, Conggong , Chen, Guodong . Efficient helmet detection based on deep learning and pruning methods [J]. | JOURNAL OF ELECTRONIC IMAGING , 2025 , 34 (2) . |
| MLA | Zhang, Yushi 等. "Efficient helmet detection based on deep learning and pruning methods" . | JOURNAL OF ELECTRONIC IMAGING 34 . 2 (2025) . |
| APA | Zhang, Yushi , Lin, Conggong , Chen, Guodong . Efficient helmet detection based on deep learning and pruning methods . | JOURNAL OF ELECTRONIC IMAGING , 2025 , 34 (2) . |
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建筑工人目标检测对于提升建筑施工安全具有重要的应用价值.随着智慧工地的推广,施工区域的视频监控覆盖率不断增加,获取大量未标注的建筑工人图像变得更为便捷,而有标注数据图像依然稀缺而昂贵.半监督学习方法是解决有标注数据缺乏问题的有效办法.然而,施工环境中存在着环境混乱、目标遮挡以及监控画面可视度低等问题,导致半监督目标检测模型在伪标签生成阶段难以平衡数量与质量.已有的半监督目标检测算法大多基于两阶段目标检测模型设计,未能满足对建筑工人检测实时性的要求.为了解决上述问题,提出了一种针对施工场景设计的单阶段半监督建筑工人目标检测算法.首先,将半监督目标检测应用于建筑工人目标检测任务,有效解决了标注数据缺乏的问题.其次,提出软阈值优化方法,为低置信样本分配权重,从而扩充伪标签的数量.接着,引入图像信息熵概念来评估样本检测难度,并提出自适应阈值选择算法以根据样本难度调整伪标签的阈值,进而提高训练初期的伪标签质量.最后,通过增加残差特征金字塔网络和上下文增强模块提升对小目标的检测能力.实验证明,在自建的施工区域建筑工人检测数据集上,所提出的算法在解决单阶段半监督建筑工人目标检测问题方面表现出显著优势.
Keyword ：
半监督目标检测 半监督目标检测 单阶段目标检测 单阶段目标检测 建筑工人目标检测 建筑工人目标检测 数量质量权衡 数量质量权衡
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| GB/T 7714 | 方莉 , 赵志峰 , 严铮 et al. 基于单阶段半监督目标检测的建筑工人检测算法 [J]. | 微电子学与计算机 , 2025 , 42 (2) : 20-30 . |
| MLA | 方莉 et al. "基于单阶段半监督目标检测的建筑工人检测算法" . | 微电子学与计算机 42 . 2 (2025) : 20-30 . |
| APA | 方莉 , 赵志峰 , 严铮 , 戴振国 , 陈国栋 . 基于单阶段半监督目标检测的建筑工人检测算法 . | 微电子学与计算机 , 2025 , 42 (2) , 20-30 . |
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Accurately identifying correct correspondences (inliers) in two-view images is a fundamental task in computer vision. Recent studies usually adopt Graph Neural Networks or stack local graphs into global ones to establish neighborhood relations. However, the smoothing properties of Graph Convolutional Neural network (GCN) cause the model to fall into local extreme, which leads to the issue of indistinguishability between inliers and outliers. Especially when the initial correspondences contain a large number of incorrect correspondences (outliers), these studies suffer from severe performance degradation. To address the above issues and refocus perspective information on distinct features, we design a Consistency Guided ResFormer Network (CGR-Net) that uses consistent correspondences to guide model perspective focusing, thereby avoiding the negative impact of outliers. Specifically, we design an efficient Graph Score Calculation module, which aims to compute global graph scores by enhancing the representation of important features and comprehensively capturing the contextual relationships between correspondences. Then, we propose a Consistency Guided Correspondences Selection module to dynamically fuse global graph scores and consistency graphs and construct a novel consistency matrix to accurately recognize inliers. Extensive experiments on various challenging tasks demonstrate that our CGR-Net outperforms state-of-the-art methods. Our code is released at https://github.com/XiaojieLi11/CGR-Net.
Keyword ：
Accuracy Accuracy consistency consistency Convolutional neural networks Convolutional neural networks Feature extraction Feature extraction Feature matching Feature matching Forestry Forestry graph convolutional neural network graph convolutional neural network outlier removal outlier removal Pipelines Pipelines pose estimation pose estimation Smoothing methods Smoothing methods Task analysis Task analysis
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| GB/T 7714 | Yang, Changcai , Li, Xiaojie , Ma, Jiayi et al. CGR-Net: Consistency Guided ResFormer for Two-View Correspondence Learning [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (12) : 12450-12465 . |
| MLA | Yang, Changcai et al. "CGR-Net: Consistency Guided ResFormer for Two-View Correspondence Learning" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34 . 12 (2024) : 12450-12465 . |
| APA | Yang, Changcai , Li, Xiaojie , Ma, Jiayi , Zhuang, Fengyuan , Wei, Lifang , Chen, Riqing et al. CGR-Net: Consistency Guided ResFormer for Two-View Correspondence Learning . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (12) , 12450-12465 . |
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This study examines the use of UAV technology to capture images of construction crane bolts and process them for denoising and enhancement. Traditional image acquisition methods often struggle to capture clear bolt images due to the variability of the construction site environment. To address this issue, this study employs UAVs for automated image acquisition, resulting in significantly improved efficiency and image quality. To tackle potential noise and illumination issues in the resulting images, this paper presents a range of image pre-processing techniques. These include median filtering, mean filtering, Gaussian filtering, and efficient denoising methods such as the C-BM3D algorithm. Additionally, histogram equalization and image enhancement strategies based on the Retinex theory are employed to further optimize image quality. The experimental results indicate that both the C-BM3D algorithm and the improved Retinex algorithm have a significant effect on enhancing image clarity. This study’s findings are important not only for improving the safety and efficiency of construction sites but also for advancing image processing technology. © 2024 SPIE.
Keyword ：
Anchor bolts Anchor bolts Cranes Cranes Image acquisition Image acquisition Image denoising Image denoising Image enhancement Image enhancement Image quality Image quality Median filters Median filters Wiener filtering Wiener filtering
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| GB/T 7714 | Chen, Guodong , Guo, Mingtao , Lin, Yuxiang et al. Study on denoising and enhancement of crane bolt images based on UAV acquisition [C] . 2024 . |
| MLA | Chen, Guodong et al. "Study on denoising and enhancement of crane bolt images based on UAV acquisition" . (2024) . |
| APA | Chen, Guodong , Guo, Mingtao , Lin, Yuxiang , Mu, Honglin , Yu, Wenlong , Jin, Xing et al. Study on denoising and enhancement of crane bolt images based on UAV acquisition . (2024) . |
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Tunnel images are affected by the shooting environment, and there are problems such as uneven light distribution, local occlusion, and more noise, etc. Aiming at the overexposure and distortion of the existing image enhancement algorithms in the optimisation process, we propose a tunnel image enhancement algorithm DNO-SCI (denoising and overexposure suppression based Self-Calibrated illumination). Firstly, based on the SCI model, a noise suppression module based on a priori knowledge is added to effectively suppress the noise of SCI after low-light enhancement. Secondly, overexposure suppression is guided through the Y channel, and finally a lightweight self-calibrated tunnel construction image enhancement algorithm is proposed in combination with depth-separable convolution. Experimental results demonstrate that the proposed image enhancement algorithm can effectively enhance tunnel construction images with uneven brightness and suppress local overexposure. © 2024 SPIE.
Keyword ：
Convolutional neural networks Convolutional neural networks Echo suppression Echo suppression Image denoising Image denoising Image enhancement Image enhancement Tunneling (excavation) Tunneling (excavation)
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| GB/T 7714 | Chen, Guodong , Xu, Han , Wu, Yanfei et al. Image enhancement algorithm for tunnel construction scenes [C] . 2024 . |
| MLA | Chen, Guodong et al. "Image enhancement algorithm for tunnel construction scenes" . (2024) . |
| APA | Chen, Guodong , Xu, Han , Wu, Yanfei , Xiong, Haining , Mu, Honglin , Lin, Jinxun et al. Image enhancement algorithm for tunnel construction scenes . (2024) . |
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This research provides an enhanced YOLOv5s tunnel workers identification algorithm to address the challenge of complicated and challenging worker distribution in tunnel environments. The network's ability to extract features is improved by incorporating a tiny target detection layer and the Squeeze-and-Excitation (SE) attention mechanism network. The introduction of depthwise separable convolution helps to prevent having a lot of model parameters. In order to decrease the missing likelihood of workers detection, the Soft-NMS algorithm is implemented with the aim of overlapping workers' target features in certain photos taken during tunnel construction. The experimental results demonstrate that the tunnel worker detection method proposed in this paper can successfully detect workers in the tunnel construction environment, with the precision rate of the improved detection model increasing from 90.06% to 94.03%, the recall rate increasing from 88.28% to 92.18%, and the average precision increasing from 83.95% to 86.91%. It is of vital significance to the life safety of tunnel workers. © 2024 SPIE.
Keyword ：
Computer vision Computer vision Object detection Object detection Object recognition Object recognition Tunneling (excavation) Tunneling (excavation)
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| GB/T 7714 | Chen, Guodong , Wang, Xiaoting , Huang, Meiling et al. Tunnel worker detection method based on improved YOLOv5s [C] . 2024 . |
| MLA | Chen, Guodong et al. "Tunnel worker detection method based on improved YOLOv5s" . (2024) . |
| APA | Chen, Guodong , Wang, Xiaoting , Huang, Meiling , Mu, Honglin , Yu, Wenlong , Jin, Xing et al. Tunnel worker detection method based on improved YOLOv5s . (2024) . |
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塔吊随着服役年限的增加,易出现裂缝、氧化腐蚀等问题,导致承载力不足,引发严重危害性的事故.传统的塔吊检测靠人工主观观察,具有很大的局限性.因此需要更加智能化的塔吊巡检方式.针对应用无人机对塔式起重机的巡检问题,提出了一种塔吊局部巡检路径生成的方法.首先训练改进的YOLOV9目标检测算法实现无人机对塔吊的识别,并对识别出的塔吊使用单目测距算法进行定位;其次提取出塔吊的骨架,对骨架离散化后聚类拟合;最后得出无人机的局部巡检路径.实验结果表明该方法可以实现塔吊身和塔吊臂的路径生成,且运行的平均时间为1.14 s,满足实时检测要求.
Keyword ：
YOLOV9算法 YOLOV9算法 单目测距 单目测距 塔吊检测 塔吊检测 聚类 聚类 骨架提取 骨架提取
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| GB/T 7714 | 刘广帅 , 陈国栋 , 陈文铿 et al. 基于改进YOLOV9的塔吊局部巡检路径生成方法 [J]. | 佳木斯大学学报（自然科学版） , 2024 , 42 (12) : 21-25 . |
| MLA | 刘广帅 et al. "基于改进YOLOV9的塔吊局部巡检路径生成方法" . | 佳木斯大学学报（自然科学版） 42 . 12 (2024) : 21-25 . |
| APA | 刘广帅 , 陈国栋 , 陈文铿 , 熊海宁 , 牟宏霖 , 林进浔 . 基于改进YOLOV9的塔吊局部巡检路径生成方法 . | 佳木斯大学学报（自然科学版） , 2024 , 42 (12) , 21-25 . |
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桥梁病害的检测对于确保公共安全和社会稳定至关重要,然而传统人工检测方法存在效率低下、错检、漏检等问题,针对这一挑战,提出了一种基于YOLO-Bridge的桥梁病害检测方法.YOLO-Bridge是基于YOLOv5的改进模型:1)引入轻量级上采样算子CARAFE,增强模型对桥梁病害关键特征的提取能力;2)采用双向特征金字塔网络BiFPN,提高模型在小目标检测和多尺度特征融合方面的表现;3)将ECA注意力机制与C3模块采用全新的融合处理方式,加强卷积层对输入特征的敏感性.另外,构建了桥梁病害数据集,并采用数据增强技术提高模型泛化能力.实验结果表明,YOLO-Bridge的mAP比原来的YOLOv5提高了 6.5％,此外,与Faster-RCNN,SSD,YOLOv3,YOLOv7-tiny等当前流行的目标检测算法相比,YOLO-Bridge在保持模型轻量的同时,实现了更高的检测精度.
Keyword ：
BiFPN BiFPN CARAFE CARAFE ECA ECA YOLOv5 YOLOv5 数据增强 数据增强
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| GB/T 7714 | 张雨诗 , 陈国栋 , 林聪功 et al. 基于YOLO-Bridge的桥梁病害检测方法研究 [J]. | 佳木斯大学学报（自然科学版） , 2024 , 42 (11) : 13-17,91 . |
| MLA | 张雨诗 et al. "基于YOLO-Bridge的桥梁病害检测方法研究" . | 佳木斯大学学报（自然科学版） 42 . 11 (2024) : 13-17,91 . |
| APA | 张雨诗 , 陈国栋 , 林聪功 , 牟宏霖 , 熊海宁 , 林进浔 . 基于YOLO-Bridge的桥梁病害检测方法研究 . | 佳木斯大学学报（自然科学版） , 2024 , 42 (11) , 13-17,91 . |
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为了解决传统钢筋间距测量方法在施工现场复杂背景和噪声下精度不佳的问题,利用YOLO-Pose算法提出了一种新的检测方法.该方法通过深度学习技术识别钢筋交点关键点,相比传统方法具备更高的鲁棒性和适应性.经过对不同检测网络的比较,YOLOv8-Pose模型在钢筋交点检测任务中表现出色,关键点检测平均准确率mAP-kp达到99.3％,FPS为77.实验结果显示,该方法通过像素标定和直径检测,能够精确计算钢筋间距,平均相对误差为2.6％,最大相对误差为8.9％,符合GB50204-2015混凝土结构工程施工质量验收规范标准.
Keyword ：
YOLO-Pose YOLO-Pose 像素标定 像素标定 关键点识别 关键点识别 深度学习 深度学习 钢筋间距检测 钢筋间距检测
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| GB/T 7714 | 林聪功 , 陈国栋 , 林鸿强 et al. 基于YOLO-Pose的钢筋间距检测 [J]. | 佳木斯大学学报（自然科学版） , 2024 , 42 (11) : 65-70 . |
| MLA | 林聪功 et al. "基于YOLO-Pose的钢筋间距检测" . | 佳木斯大学学报（自然科学版） 42 . 11 (2024) : 65-70 . |
| APA | 林聪功 , 陈国栋 , 林鸿强 , 张雨诗 , 牟宏霖 , 林进浔 et al. 基于YOLO-Pose的钢筋间距检测 . | 佳木斯大学学报（自然科学版） , 2024 , 42 (11) , 65-70 . |
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