An Algorithm for Target Detection of Engineering Vehicles Based on Improved CenterNet

Yu, Pingping; Wang, Hongda; Zhao, Xiaodong; Ruan, Guangchen

doi:10.32604/cmc.2022.029239

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…The loss function L k of the centre point and classification is the focal loss function, and the calculation formula is shown in Equation (1) [32].…”

Section: Centernetmentioning

confidence: 99%

Crack Detection of Concrete Based on Improved CenterNet Model

Kang,

Zhou,

Gao

et al. 2024

Applied Sciences

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Cracks on concrete surfaces are vital factors affecting construction safety. Accurate and efficient crack detection can prevent safety-related accidents. Using drones to photograph cracks on a concrete surface and detect them through computer vision technology has the advantages of accurate target recognition, simple practical operation, and low cost. To solve this problem, an improved CenterNet concrete crack-detection model is proposed. Firstly, a channel-space attention mechanism is added to the original model to enhance the ability of the convolution neural network to pay attention to the image. Secondly, a feature selection module is introduced to scale the feature map in the downsampling stage to a uniform size and combine it in the channel dimension. In the upsampling stage, the feature selection module adaptively selects the combined features and fuses them with the output features of the upsampling. Finally, the target size loss is optimized from a Smooth L1 Loss to IoU Loss to lessen its inability to adapt to targets of different sizes. The experimental results show that the improved CenterNet model reduces the FPS by 123.7 Hz, increases the GPU memory by 62 MB, increases the FLOPs by 3.81 times per second, and increases the AP by 15.4% compared with the original model. The GPU memory occupancy remained stable during the training process and exhibited good real-time performance and robustness.

show abstract

“…The loss function L k of the centre point and classification is the focal loss function, and the calculation formula is shown in Equation (1) [32].…”

Section: Centernetmentioning

confidence: 99%

Crack Detection of Concrete Based on Improved CenterNet Model

Kang,

Zhou,

Gao

et al. 2024

Applied Sciences

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show abstract

“…The results show that the proposed method effectively extracts the feature information of small objects and improves the small object detection accuracy of the YOLOv5 algorithm. In order to compare the performance of each algorithm, Table 4 lists the specific de- In order to compare the performance of each algorithm, Table 4 lists the specific detection results of the latest small object detection algorithms, such as: RetinaNet [16], RefineNet [17], DetNet [18], CornerNet [19], IENet [20], DMNET [21], CascadeNet [22], GLSAN [23] and CenterNeSt [24] on the VisDrone-Det2021 dataset. In view of all precision data in Table 4, we can notice that the accuracy of the proposed algorithm is superior to the other state-of-the-art algorithms.…”

Section: Performance Evaluationmentioning

confidence: 99%

Small Object Detection Method Based on Weighted Feature Fusion and CSMA Attention Module

et al. 2022

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Small object detection is one of the challenging tasks in computer vision. Most of the existing small object detection models cannot fully extract the characteristics of small objects within an image, due to the small coverage area, low resolution and unclear detailed information of small objects in the image; hence, the effect of these models is not ideal. To solve this problem, a simple and efficient reinforce feature pyramid network R-FPN is proposed for the YOLOv5 algorithm. The learnable weight is introduced to show the importance of different input features, make full use of the useful information of different feature layers and strengthen the extraction of small object features. At the same time, a channel space mixed attention CSMA module is proposed to extract the detailed information of small objects combined with spaces and channels, suppress other useless information and further improve the accuracy of small object detection. The experimental results show that the proposed method improves the average accuracy AP, AP50 and AR100 of the original algorithm by 2.11%, 2.86% and 1.94%, respectively, and the detection effect is better than the existing small object detection algorithms, which proves the effectiveness of the proposed method.

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“…In this way, detection efficiency can be improved while maintaining safety, and the size and location of bridge cracks can be more accurately recorded. Recently, some researchers have applied deep learning based on object detection [17][18][19][20][21][22][23], semantic segmentation [24,25] and instance segmentation [26,27] to automate bridge crack detection, and remarkable progress has been made. By learning the mapping in the image, cracks can be accurately and efficiently identified, extracted and segmented from the complex background, providing technical support for fast and accurate bridge crack detection.…”

Section: Introductionmentioning

confidence: 99%

Coarse–Fine Combined Bridge Crack Detection Based on Deep Learning

Ma,

Hao,

Meng

et al. 2024

Applied Sciences

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The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposes a coarse–fine combined bridge crack detection method of “double detection + single segmentation” based on deep learning. To validate the effect and practicality of fine crack detection, images of old civil bridges and viaduct bridges against a complex background and images of a bridge crack against a simple background are used as datasets. You Only Look Once V5(x) (YOLOV5(x)) was preferred as the object detection network model (ODNM) to perform initial and fine detection of bridge cracks, respectively. Using U-Net as the optimal semantic segmentation network model (SSNM), the crack detection results are accurately segmented for fine crack detection. The test results showed that the initial crack detection using YOLOV5(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOV5(x) was adopted for fine crack detection, which can determine the location and shape of cracks more carefully and accurately. Finally, the U-Net model was used to segment the accurately detected cracks and achieved a maximum accuracy (AC) value of 98.37%. The experiment verifies the effectiveness and accuracy of this method, which not only provides a faster and more accurate method for fine detection of bridge cracks but also provides technical support for future automated detection and preventive maintenance of bridge structures and has practical value for bridge crack detection engineering.

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An Algorithm for Target Detection of Engineering Vehicles Based on Improved CenterNet

Cited by 6 publications

References 10 publications

Crack Detection of Concrete Based on Improved CenterNet Model

Crack Detection of Concrete Based on Improved CenterNet Model

Small Object Detection Method Based on Weighted Feature Fusion and CSMA Attention Module

Coarse–Fine Combined Bridge Crack Detection Based on Deep Learning

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