YOLOv5s FMG: An Improved Small Target Detection Algorithm Based on YOLOv5 in Low Visibility

Zheng, Yunchang; Zhan, Yongzhao; Huang, Xiaoying; Ji, Gaoqing

doi:10.1109/access.2023.3297218

Cited by 10 publications

(3 citation statements)

References 30 publications

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“…We have two classification results for the detection of mask wear: positive and negative samples of face objects with and without masks. The experiments in this paper use recall (R), precision (P), average precision (AP), and average precision mean(mAP) to evaluate the accuracy of the detection model, the parameters, and the number of computation FLOPs to evaluate the lightweight level of the model and the detection time to evaluate the real-time nature of the model [28].…”

Section: B Evaluation Indicatorsmentioning

confidence: 99%

YOLOv5-S2C2: An Improved Method of Mask Detection Based on Lightweight

Xie,

Ma,

et al. 2024

IEEE Access

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Masks play a crucial role in preventing respiratory diseases and have diverse applications in national public health and industrial safety. Efficient mask-wearing detection systems are essential for ensuring accuracy and real-time performance. To overcome the challenges of extensive model calculations, extensive parameter volume, and complex hardware deployment in the current mask-wearing detection system, a lightweight mask detection model with improved YOLOv5 is proposed. Firstly, this study proposes a new lightweight network-EMA-FasterNet, as the backbone network of YOLOv5, which reduces the computation while preserving the information of each channel. Secondly, using Depthwise Separable Convolutions (DepthSepConv) to replace some C3 modules in the Neck further compresses the model volume, parameters, and computation. Finally, to prevent the detection of missing objects due to the removal of overlapping candidate boxes, use Soft Non-Maximum Suppression (NMS) to replace NMS. Compared with YOLOv5s, the proposed YOLOv5-S2C2 has high mAP in both Dataset I and Dataset II, while the parameters and computation of the model are reduced by about 56% and 57.0%, respectively. The volume is about 44% of the original model and the inference speed on GPU is improved by about 30%. These improvements show that the proposed model achieves a lightweight design and excellent real-time performance. It is also well suited for efficient use on hardware with limited computational resources.

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Section: B Evaluation Indicatorsmentioning

confidence: 99%

YOLOv5-S2C2: An Improved Method of Mask Detection Based on Lightweight

Xie,

Ma,

et al. 2024

IEEE Access

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“…Li et al [18] utilized the RegNetY network for the intelligent recognition of defects in sewer pipeline networks, which can improve detection efficiency and have a stronger classification ability. The neural network framework based on YOLO has a fast detection speed, can detect small targets, and significantly improves detection accuracy and efficiency [19]. In 2020, Li et al [20] used the YOLOv3 neural network built using the TensorFlow1.13.0 framework developed by Google to detect GPR images in real-time.…”

Section: Introductionmentioning

confidence: 99%

Underground Defects Detection Based on GPR by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8

Wang,

Zhang,

et al. 2024

IEEE Access

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Ground Penetrating Radar (GPR) is an effective non-destructive detection method, that is frequently utilized in the detection of urban underground defects because of its quick speed, convenient and flexible operation, and high resolution. However, there are some limitations to defect detection using GPR, such as less data, poor data quality, and complexity of data interpretation. In this study, an underground defect detection system based on GPR was established. First, a Simple Linear Iterative Clustering (SLIC)-PHash, a Data Augmentation (DA) optimization algorithm, was created to obtain high-quality datasets. Second, the Convolutional Block Attention Module (CBAM)-YOLOv8, a detection model, was produced for the recognition of defects. This model uses GhostConv and CBAM to create a lighter design that better focuses on target detection and increases efficiency. Finally, a one-click detection system was formed by fusing SLIC-Phsh and CBAM-YOLOv8, which were used for one-click GPR dataset optimization and defect detection.The developed system has the best detection mAP and F1 scores of 90.8% and 88.3%, respectively, compared to several well-known Deep Learning (DL)-based techniques. The results demonstrated that the system proposed in this paper can greatly improve detection efficiency and reduce detection time by achieving a good balance between detection speed and accuracy.

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“…Hamzenejadi and Mohseni (2023) optimized YOLOv5 for real-time vehicle detection in UAV imagery by incorporating architectural enhancements that markedly improved performance [25]. Zheng et al (2023) introduced YOLOv5s FMG, a refined algorithm aimed at detecting small targets in low visibility conditions, which enhances accuracy in challenging environments [26]. Despite these advancements, there remains a gap in multi-scale object detection research, highlighting the need for algorithms capable of effectively detecting and tracking objects of various sizes across diverse environments.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Target Detection in Autonomous Driving Scenarios Based on YOLOv5-AFAM

Ma,

Zhao,

Liu

et al. 2024

Applied Sciences

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Multi-scale object detection is critically important in complex driving environments within the field of autonomous driving. To enhance the detection accuracy of both small-scale and large-scale targets in complex autonomous driving environments, this paper proposes an improved YOLOv5-AFAM algorithm. Firstly, the Adaptive Fusion Attention Module (AFAM) and Down-sampling Module (DownC) are introduced to increase the detection precision of small targets. Secondly, the Efficient Multi-scale Attention Module (EMA) is incorporated, enabling the model to simultaneously recognize small-scale and large-scale targets. Finally, a Minimum Point Distance IoU-based Loss Function (MPDIou-LOSS) is introduced to improve the accuracy and efficiency of object detection. Experimental validation on the KITTI dataset shows that, compared to the baseline model, the improved algorithm increased precision by 2.4%, recall by 2.6%, mAP50 by 1.5%, and mAP50-90 by an impressive 4.8%.

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YOLOv5s FMG: An Improved Small Target Detection Algorithm Based on YOLOv5 in Low Visibility

Cited by 10 publications

References 30 publications

YOLOv5-S2C2: An Improved Method of Mask Detection Based on Lightweight

YOLOv5-S2C2: An Improved Method of Mask Detection Based on Lightweight

Underground Defects Detection Based on GPR by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8

Multi-Scale Target Detection in Autonomous Driving Scenarios Based on YOLOv5-AFAM

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