G-YOLOX: A Lightweight Network for Detecting Vehicle Types

Luo, Qiang; Wang, Junfan; Gao, Mingyu; Lin, Huipin; Zhou, Hongtao; Miao, Qiheng

doi:10.1155/2022/4488400

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…In several groups of comparative experiments, we compared the improved attention mechanism with ones reported in the literature. We verified our method on several challenging computer vision tasks, such as object detection on a car dataset VOC2019 [30] that we created, PASCAL VOC [31], bdd100k datasets [32], as well as. We used the YOLOv5s network as the basic network.…”

Section: Experiments Environmentmentioning

confidence: 98%

“…From the perspective of the design of the network structure, CSPNet solves the problem of a large amount of calculations in the reasoning process encountered in past work, as shown in Figure 2. It uses two methods to obtain an output feature map; one part uses a 1 × 1 convolution, and the other part uses a 3 × 3 convolution and the ResNet [30] network. The results of the two convolutions are fused to generate the output feature map of this module.…”

Section: G_csp Modulementioning

confidence: 99%

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Multiple Mechanisms to Strengthen the Ability of YOLOv5s for Real-Time Identification of Vehicle Type

et al. 2022

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Identifying the type of vehicle on the road is a challenging task, especially in the natural environment with all its complexities, such that the traditional architecture for object detection requires an excessively large amount of computation. Such lightweight networks as MobileNet are fast but cannot satisfy the performance-related requirements of this task. Improving the detection-related performance of small networks is, thus, an outstanding challenge. In this paper, we use YOLOv5s as the backbone network to propose a large-scale convolutional fusion module called the ghost cross-stage partial network (G_CSP), which can integrate large-scale information from different feature maps to identify vehicles on the road. We use the convolutional triplet attention network (C_TA) module to extract attention-based information from different dimensions. We also optimize the original spatial pyramid pooling fast (SPPF) module and use the dilated convolution to increase the capability of the network to extract information. The optimized module is called the DSPPF. The results of extensive experiments on the bdd100K, VOC2012 + 2007, and VOC2019 datasets showed that the improved YOLOv5s network performs well and can be used on mobile devices in real time.

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Section: Experiments Environmentmentioning

confidence: 98%

Section: G_csp Modulementioning

confidence: 99%

Multiple Mechanisms to Strengthen the Ability of YOLOv5s for Real-Time Identification of Vehicle Type

et al. 2022

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“…Considering the rapid update speed of automotive products and the large number of components, using a recognition method that only contains a single recognized target in an image can avoid the increase in cost caused by the manual data annotation described in [12,13,16] and is more in line with the efciency requirements of manufacturing enterprises. For the problem of insufcient learning samples, some researchers propose to use Gan [18] to increase sample data.…”

Section: Related Workmentioning

confidence: 99%

Automobile Component Recognition Based on Deep Learning Network with Coarse-Fine-Grained Feature Fusion

Tan

Xia

2023

International Journal of Intelligent Systems

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With the development of artificial intelligence, machine vision technology based on deep learning is an effective way to improve production efficiency. Because of the rapid update of the automobile manufacturing industry and the large variety of products, the learning time and the number of learning samples of the deep learning model are limited, which brings great difficulties to the recognition of components. Therefore, considering the economic benefits of enterprises, this paper proposes an intelligent component recognition method appropriate for small datasets, aiming to explore an automatic system for component recognition suitable for industrial manufacturing environments. The method completes the generation of the dataset through the system architecture with the potential for automation and the image cropping method based on feature detection and then designs a deep learning network based on coarse-fine-grained feature fusion to generate an intelligent recognition model of components. Finally, the designed network achieves an accuracy of 95.11%, and compared with the traditional classical network on multiple datasets, the designed network has better performance. Thus, the proposed method can improve the production flexibility of the automobile manufacturing industry and improve equipment intelligence.

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“…A study by Stork et al [ 43 ] used YOLO V3 to detect microseismic signals collected by a DAS system with precision exceeding that of manual detection by 80%, with only a 2% false detection rate. Luo et al [ 44 ] designed a lightweight detection network called G-YOLOX for vehicle type detection. It is suitable for practical applications with an embedded device.…”

Section: Introductionmentioning

confidence: 99%

A YOLOX-Based Automatic Monitoring Approach of Broken Wires in Prestressed Concrete Cylinder Pipe Using Fiber-Optic Distributed Acoustic Sensors

Gao

Zhang

et al. 2023

Sensors

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Wire breakage is a major factor in the failure of prestressed concrete cylinder pipes (PCCP). In the presented work, an automatic monitoring approach of broken wires in PCCP using fiber-optic distributed acoustic sensors (DAS) is investigated. The study designs a 1:1 prototype wire break monitoring experiment using a DN4000 mm PCCP buried underground in a simulated test environment. The test combines the collected wire break signals with the previously collected noise signals in the operating pipe and transforms them into a spectrogram as the wire break signal dataset. A deep learning-based target detection algorithm is developed to detect the occurrence of wire break events by extracting the spectrogram image features of wire break signals in the dataset. The results show that the recall, precision, F1 score, and false detection rate of the pruned model reach 100%, 100%, 1, and 0%, respectively; the video detection frame rate reaches 35 fps and the model size is only 732 KB. It can be seen that this method greatly simplifies the model without loss of precision, providing an effective method for the identification of PCCP wire break signals, while the lightweight model is more conducive to the embedded deployment of a PCCP wire break monitoring system.

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G-YOLOX: A Lightweight Network for Detecting Vehicle Types

Cited by 6 publications

References 22 publications

Multiple Mechanisms to Strengthen the Ability of YOLOv5s for Real-Time Identification of Vehicle Type

Multiple Mechanisms to Strengthen the Ability of YOLOv5s for Real-Time Identification of Vehicle Type

Automobile Component Recognition Based on Deep Learning Network with Coarse-Fine-Grained Feature Fusion

A YOLOX-Based Automatic Monitoring Approach of Broken Wires in Prestressed Concrete Cylinder Pipe Using Fiber-Optic Distributed Acoustic Sensors

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