A smoking behavior detection method based on the YOLOv5 network

Jiang, Xiangkui; Hu, Haochang; Liu, Xun; Ding, Rui; Xu, Yuanbo; Shi, Jianxu; Du, Yunyan; Da, Chunlin

doi:10.1088/1742-6596/2232/1/012001

Cited by 14 publications

(10 citation statements)

References 13 publications

(11 reference statements)

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“…Furthermore, the Large Kernel Attention (LKA) mechanism enables adaptability in both spatial and channel dimensions, enhancing the perceptual field capture and remote dependence 12 . Studies also shown that fusing YOLOv4 with CBAM 13,14 , as well as integrating SE-Net into YOLOv5 15 , significantly improved detection accuracy and recall. These studies demonstrate that feature enhancement based on attention mechanisms can effectively improve feature map representation and detection performance.…”

Section: Related Workmentioning

confidence: 93%

DETrack: Multi-object tracking algorithm based on feature decomposition and feature enhancement

Wen

Huang

et al. 2023

Preprint

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Multi-object tracking (MOT) algorithms are typically classified as one-shot or two-step algorithms. The one-shot MOT algorithm is widely studied and applied due to its fast inference speed. However, one-shot algorithms include two sub-tasks of detection and re-ID, which have conflicting directions for model optimization, thus limiting tracking performance. Additionally, MOT algorithms often suffer from serious ID switching issues, which can negatively affect the tracking effect. To address these challenges, this study proposes the DETrack algorithm, which consists of feature decomposition and feature enhancement modules. The feature decomposition module can effectively exploit the differences and correlations of different tasks to solve the conflict problem. Moreover, it can effectively mitigate the competition between the detection and re-ID tasks, while simultaneously enhancing their cooperation. The feature enhancement module can improve feature quality and alleviate the problem of target ID switching. Experimental results demonstrate that DETrack has achieved improvements in multi-object tracking performance, while reducing the number of ID switching. The designed method of feature decomposition and feature enhancement can significantly enhance target tracking effectiveness.

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Section: Related Workmentioning

confidence: 93%

DETrack: Multi-object tracking algorithm based on feature decomposition and feature enhancement

Wen

Huang

et al. 2023

Preprint

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“…To further validate the performance of the method in this paper, a comparison test is set up to compare the algorithm in this paper with the mainstream algorithms in the field of target detection under the same experimental environment. Faster R-CNN, a representative of two-stage target detection algorithms, and SSD, YOLOv3 [15] , YOLOv4-tiny [16] , and YOLOv5, representatives of one-stage target detection algorithms, were selected. The experimental results are shown in Table 6.…”

Section: Ablation Experimentsmentioning

confidence: 99%

A biscit defect detection method based on improved YOLOv5

2023

AJCIS

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Defect detection is of great importance to ensure the quality of biscuit production. An improved YOLOv5 biscuit detection algorithm is proposed for the problems of poor real-time and low accuracy of biscuit defect detection methods. First, the number of C3s in the backbone network is reduced, and then the depth-separable convolution is used instead of the normal convolution in the network to reduce the model parameters and computation and improve the detection speed. Secondly, the SE attention module is added to the feature extraction layer to enhance the feature extraction capability of the backbone network and improve the accuracy of biscuit defect detection. Finally, the EIOU loss function is introduced to accelerate the model convergence and accurate target localization. The improved algorithm is tested on the self-built biscuit dataset, and the experimental results show that: the detection accuracy of the proposed algorithm can reach 99.2%, and the detection speed is 67 frames/s, which can meet the actual production requirements.

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“…They set the training set (validation set + training set) and the test set of the FER dataset at 80.0% and 20.0%, respectively [8]. Xiangkui Jiang et al proposed a smoking behavior detection method based on YOLOv5 [9], using a homemade smoking behavior dataset for training and setting the proportion of training set to test set to 7:3. Fei Yang et al proposed a street image external air conditioner unit detection method [10], setting the homemade air conditioner external unit dataset in the proportion of training set (validation set + training set) and test set as 7.4:2.6.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, datasets have a very important role in tasks, such as object detection, and they can affect the accuracy of the analysis results, but there is no uniform method for dividing the proportion of datasets. Sangwon Kim et al [8,[10][11][12] divided the dataset into a training set, as well as a test set; Jia Yao et al [9,13,14,18] divided the dataset into a training set, validation set, and test set. Kai Huang et al [13,14,18] used dataset partitioning proportions recommended by neural network authors, and Sangwon Kim et al [7][8][9][10][11][12]18] did not specify the method of dataset grouping proportions.…”

Section: Introductionmentioning

confidence: 99%

“…Sangwon Kim et al [8,[10][11][12] divided the dataset into a training set, as well as a test set; Jia Yao et al [9,13,14,18] divided the dataset into a training set, validation set, and test set. Kai Huang et al [13,14,18] used dataset partitioning proportions recommended by neural network authors, and Sangwon Kim et al [7][8][9][10][11][12]18] did not specify the method of dataset grouping proportions. It can be seen that scholars have grouped the datasets in such a way that the proportion of the training set to the dataset as a whole varies between 60% and 98%, and the effect of different percentages of the training set in the dataset on the performance of the network model needs further study.…”

Section: Introductionmentioning

confidence: 99%

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A TEDE Algorithm Studies the Effect of Dataset Grouping on Supervised Learning Accuracy

Wang

Song

et al. 2023

Electronics

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Datasets are the basis for research on deep learning methods in computer vision. The impact of the percentage of training sets in a dataset on the performance of neural network models needs to be further explored. In this paper, a twice equal difference enumeration (TEDE) algorithm is proposed to investigate the effect of different training set percentages in the dataset on the performance of the network model, and the optimal training set percentage is determined. By selecting the Pascal VOC dataset and dividing it into six different datasets from largest to smallest, and then dividing each dataset into the datasets to be analyzed according to five different training set percentages, the YOLOv5 convolutional neural network is used to train and test the 30 datasets to determine the optimal neural network model corresponding to the training set percentages. Finally, tests were conducted using the Udacity Self-Driving dataset with a self-made Tire Tread Defects (TTD) dataset. The results show that the network model performance is superior when the training set accounts for between 85% and 90% of the overall dataset. The results of dataset partitioning obtained by the TEDE algorithm can provide a reference for deep learning research.

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A smoking behavior detection method based on the YOLOv5 network

Cited by 14 publications

References 13 publications

DETrack: Multi-object tracking algorithm based on feature decomposition and feature enhancement

DETrack: Multi-object tracking algorithm based on feature decomposition and feature enhancement

A biscit defect detection method based on improved YOLOv5

A TEDE Algorithm Studies the Effect of Dataset Grouping on Supervised Learning Accuracy

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