Safety Helmet Wearing Detection in Aerial Images Using Improved YOLOv4

Chen, Wei; Liu, Mi; Zhou, Xuhong; Pan, Jiandong; Tan, Haozhi

doi:10.32604/cmc.2022.026664

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…The convolutional block attention module (CBAM) attention mechanism is also used to make the model focus more on the main information to improve detection accuracy. Due to the small target size and the loss of safety helmet feature information brought on by network downsampling, Chen et al 29 proposed an improved YOLOv4 model to detect the wearing of safety helmets in aerial photography. Deep learning-based object detection has become a mainstream algorithm, surpassing traditional image processing algorithms in speed and accuracy.…”

Section: Safety Helmet Detection Based On Deep Learningmentioning

confidence: 99%

FM-YOLOv7: an improved detection method for mine personnel helmet

Shao

Liu

et al. 2023

J. Electron. Imag.

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.Due to the complex underground environment and the small object of the helmet, the detection accuracy is low when the original YOLOv7 algorithm is used to detect whether the mine personnel wears the helmet, which cannot be applied to the actual operation site. In response to this problem, we proposed an FM-YOLOv7 mine personnel helmet detection. First, to improve the feature extraction ability of the shallow network and enhance the representation ability of the model on the helmet, we propose the fused-MBCA (fused-MBConv with the coordinate attention) module. Second, to improve the detection ability of small objects, enable the fused features to obtain high-level semantic information and low-level details from different scales, and have more extensive receptive fields, we propose the multi-scale feature fusion efficient layer aggregation networks. Finally, to accelerate the convergence of the model and improve the regression accuracy, we use efficient intersection over union as the bounding box regression loss function. These experiments are based on the self-built mine personnel safety helmet dataset. The results show that the FM-YOLOv7 model outperformed the other six algorithms. The mAP@0.5 of the proposed model can reach 85.7%, which is 1.4% higher than the original YOLOv7 model. Also, the improved YOLOv7 model achieves 91 frames per second in detection speed, which detects whether the mine personnel wears a safety helmet in real time.

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Section: Safety Helmet Detection Based On Deep Learningmentioning

confidence: 99%

FM-YOLOv7: an improved detection method for mine personnel helmet

Shao

Liu

et al. 2023

J. Electron. Imag.

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“…Considers the edge attributes by using top-k attention mechanisms to learn hidden semantic contextual, improved network performance. Chen et al [24] presented an improved YOLOv4 algorithm, which increases the dimension of the effective feature layer of the backbone network. It introduces the cross stage partial (CSP) structure into path aggregation network (PANet).…”

Section: Introductionmentioning

confidence: 99%

RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

Zhang¹,

Deng²,

Chen³

2023

Computers, Materials &Amp; Continua

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In recent years, target detection of aerial images of unmanned aerial vehicle (UAV) has become one of the hottest topics. However, target detection of UAV aerial images often presents false detection and missed detection. We proposed a modified you only look once (YOLO) model to improve the problems arising in object detection in UAV aerial images: (1) A new residual structure is designed to improve the ability to extract features by enhancing the fusion of the inner features of the single layer. At the same time, triplet attention module is added to strengthen the connection between space and channel and better retain important feature information.(2) The feature information is enriched by improving the multi-scale feature pyramid structure and strengthening the feature fusion at different scales. (3) A new loss function is created and the diagonal penalty term of the anchor frame is introduced to improve the speed of training and the accuracy of reasoning. The proposed model is called residual feature fusion triple attention YOLO (RT-YOLO). Experiments showed that the mean average precision (mAP) of RT-YOLO is increased from 57.2% to 60.8% on the vehicle detection in aerial image (VEDAI) dataset, and the mAP is also increased by 1.7% on the remote sensing object detection (RSOD) dataset. The results show that the RT-YOLO outperforms other mainstream models in UAV aerial image object detection.

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