Real-Time Vehicle Detection from UAV Aerial Images Based on Improved YOLOv5

Li, S.; Yang, Xiaodong; Xiao, Lin; Zhang, Yanyi; Wu, Jiahui

doi:10.3390/s23125634

Cited by 20 publications

(8 citation statements)

References 42 publications

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“…The YOLOv8 algorithm represents the latest achievement in the YOLO (You Only Look Once) series of object detection algorithms [15][16][17][18][19][20][21]. The YOLO series of algorithms has found extensive applications across various engineering domains [22][23][24][25][26]. YOLOv8 builds upon the YOLOv5 algorithm, striking a balance between accuracy and detection speed.…”

Section: Yolov8 Network Modelmentioning

confidence: 99%

Real‐time defect detection method based on YOLO‐GSS at the edge end of a transmission line

Hou,

Li,

Shen

et al. 2024

IET Image Processing

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Combining edge devices with intelligent inspection for transmission lines can fulfill the demand for real‐time defect detection in the field. However, there has been limited research on algorithms suitable for edge devices with low computational power and memory, and the existing research primarily focuses on CPU optimization. To address these issues, this paper proposes a real‐time defect detection method for transmission line endpoints based on YOLO‐GSS (YOLOv8 with Mosaic‐9, G‐GhostNet, S‐FPN, and Spatial Intersection over Union (SIoU) modifications). First, the authors improve the input of the YOLOv8 network using Mosaic‐9 to increase the number of input features in the training phase and enhance algorithm robustness. Next, the authors introduce G‐GhostNet and S‐FPN to enhance the backbone and neck sections while improving inference speed and accuracy. Finally, the authors modify the Complete Intersection over Union loss function of YOLOv8 using SIoU to further improve the detection accuracy. Experimental results demonstrate that compared to the original YOLOv8, the proposed method achieves a 5x increase in inference speed on Nvidia Jetson NX edge devices and a 7.7% improvement in accuracy, meeting the real‐time defect detection requirements for transmission line field inspections.

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Section: Yolov8 Network Modelmentioning

confidence: 99%

Real‐time defect detection method based on YOLO‐GSS at the edge end of a transmission line

Hou,

Li,

Shen

et al. 2024

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…This paper extends the application of multi-scale information to even shallower feature maps, prunes redundant network structures, and employs the CARAFE module to minimize feature information loss during upsampling, along with the SPD-Conv module to preserve fine-grained feature map information. Furthermore, processing the context region of targets instead of simple pixel-by-pixel processing during training [24] yields an efficient multi-scale training approach. Another study [25] has demonstrated improved detection performance through the utilization of relevant information across different feature maps.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Small Object Detection in Aerial Images: A Novel Approach with PCSG Model

An,

Duanmu,

et al. 2024

Aerospace

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Generalized target detection algorithms perform well for large- and medium-sized targets but struggle with small ones. However, with the growing importance of aerial images in urban transportation and environmental monitoring, detecting small targets in such imagery has been a promising research hotspot. The challenge in small object detection lies in the limited pixel proportion and the complexity of feature extraction. Moreover, current mainstream detection algorithms tend to be overly complex, leading to structural redundancy for small objects. To cope with these challenges, this paper recommends the PCSG model based on yolov5, which optimizes both the detection head and backbone networks. (1) An enhanced detection header is introduced, featuring a new structure that enhances the feature pyramid network and the path aggregation network. This enhancement bolsters the model’s shallow feature reuse capability and introduces a dedicated detection layer for smaller objects. Additionally, redundant structures in the network are pruned, and the lightweight and versatile upsampling operator CARAFE is used to optimize the upsampling algorithm. (2) The paper proposes the module named SPD-Conv to replace the strided convolution operation and pooling structures in yolov5, thereby enhancing the backbone’s feature extraction capability. Furthermore, Ghost convolution is utilized to optimize the parameter count, ensuring that the backbone meets the real-time needs of aerial image detection. The experimental results from the RSOD dataset show that the PCSG model exhibits superior detection performance. The value of mAP increases from 97.1% to 97.8%, while the number of model parameters decreases by 22.3%, from 1,761,871 to 1,368,823. These findings unequivocally highlight the effectiveness of this approach.

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“…The ACMM is designed to improve the extraction of target features. In their study, Liu et al [36] incorporated GhostNet as an alternative to the conventional convolutional layer. The final backbone layer was enhanced with the SepViT module, and the channel attention mechanism (ECA) was integrated into the YOLOv5 feature extraction network.…”

Section: Related Workmentioning

confidence: 99%

ITD-YOLOv8: An Infrared Target Detection Model Based on YOLOv8 for Unmanned Aerial Vehicles

Zhao,

Zhang,

Zhang

et al. 2024

Drones

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A UAV infrared target detection model ITD-YOLOv8 based on YOLOv8 is proposed to address the issues of model missed and false detections caused by complex ground background and uneven target scale in UAV aerial infrared image target detection, as well as high computational complexity. Firstly, an improved YOLOv8 backbone feature extraction network is designed based on the lightweight network GhostHGNetV2. It can effectively capture target feature information at different scales, improving target detection accuracy in complex environments while remaining lightweight. Secondly, the VoVGSCSP improves model perceptual abilities by referencing global contextual information and multiscale features to enhance neck structure. At the same time, a lightweight convolutional operation called AXConv is introduced to replace the regular convolutional module. Replacing traditional fixed-size convolution kernels with convolution kernels of different sizes effectively reduces the complexity of the model. Then, to further optimize the model and reduce missed and false detections during object detection, the CoordAtt attention mechanism is introduced in the neck of the model to weight the channel dimensions of the feature map, allowing the network to pay more attention to the important feature information, thereby improving the accuracy and robustness of object detection. Finally, the implementation of XIoU as a loss function for boundary boxes enhances the precision of target localization. The experimental findings demonstrate that ITD-YOLOv8, in comparison to YOLOv8n, effectively reduces the rate of missed and false detections for detecting multi-scale small targets in complex backgrounds. Additionally, it achieves a 41.9% reduction in model parameters and a 25.9% decrease in floating-point operations. Moreover, the mean accuracy (mAP) attains an impressive 93.5%, thereby confirming the model’s applicability for infrared target detection on unmanned aerial vehicles (UAVs).

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Real-Time Vehicle Detection from UAV Aerial Images Based on Improved YOLOv5

Cited by 20 publications

References 42 publications

Real‐time defect detection method based on YOLO‐GSS at the edge end of a transmission line

Real‐time defect detection method based on YOLO‐GSS at the edge end of a transmission line

Enhancing Small Object Detection in Aerial Images: A Novel Approach with PCSG Model

ITD-YOLOv8: An Infrared Target Detection Model Based on YOLOv8 for Unmanned Aerial Vehicles

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