SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection

Niu, Chengwen; Song, Yunsheng; Zhao, Xinyue

doi:10.3390/app132413052

Cited by 13 publications

(3 citation statements)

References 31 publications

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“…The SPPFCSPC module draws inspiration from the concept of SPPF [ 26 , 27 ], which structurally reduces the number of times the convolution kernel size needs to be specified. While SPP requires specification of the dimensions of the convolution kernel three times to pool and splice the data from the CBS module, SPPF only requires specification of one convolution kernel.…”

Section: Methodsmentioning

confidence: 99%

A Lightweight Vehicle Detection Method Fusing GSConv and Coordinate Attention Mechanism

Huang,

Tu,

Zhang

et al. 2024

Sensors

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Aiming at the problems of target detection models in traffic scenarios including a large number of parameters, heavy computational burden, and high application cost, this paper introduces an enhanced lightweight real-time detection algorithm, which exhibits higher detection speed and accuracy for vehicle detection. This paper considers the YOLOv7 algorithm as the benchmark model, designs a lightweight backbone network, and uses the MobileNetV3 lightweight network to extract target features. Inspired by the structure of SPPF, the spatial pyramid pooling module is reconfigured by incorporating GSConv, and a lightweight SPPFCSPC-GS module is designed, aiming to minimize the quantity of model parameters and enhance the training speed even further. Furthermore, the CA mechanism is integrated to enhance the feature extraction capability of the model. Finally, the MPDIoU loss function is utilized to optimize the model’s training process. Experiments showcase that the refined YOLOv7 algorithm can achieve 98.2% mAP on the BIT-Vehicle dataset with 52.8% fewer model parameters than the original model and a 35.2% improvement in FPS. The enhanced model adeptly strikes a finer equilibrium between velocity and precision, providing favorable conditions for embedding the model into mobile devices.

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Section: Methodsmentioning

confidence: 99%

A Lightweight Vehicle Detection Method Fusing GSConv and Coordinate Attention Mechanism

Huang,

Tu,

Zhang

et al. 2024

Sensors

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show abstract

“…The improved algorithm increases the accuracy of vehicle position and tilt angle detection to reduce the computation of the model and increase the speed of vehicle position detection. Niu et al [11] made innovative modifications based on the YOLOv7 framework: the SE attention mechanism was added to the backbone module, the related module was replaced, the feature extraction of the model was enhanced, the model achieved better results and the detection capability was improved. Shao et al [12] proposed an improved YOLOv5s vehicle recognition and detection algorithm using the ELU activation function instead of the original activation function.…”

Section: Introductionmentioning

confidence: 99%

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Zhang,

Gong,

Chen

2024

WEVJ

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Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks.

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“…Infrared target detection has the benefits of all-weather, long-range, and strong antiinterference [1], so UAV-based infrared target detection has an important role in military [2], accident search and rescue [3,4], and traffic monitoring [5][6][7]. However, the aerial images captured by UAVs often contain numerous multi-scale, small targets, which typically have limited features available for extraction [8].…”

Section: Introductionmentioning

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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SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection

Cited by 13 publications

References 31 publications

A Lightweight Vehicle Detection Method Fusing GSConv and Coordinate Attention Mechanism

A Lightweight Vehicle Detection Method Fusing GSConv and Coordinate Attention Mechanism

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

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

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