Foxtail Millet Ear Detection Method Based on Attention Mechanism and Improved YOLOv5

Qiu, Shujin; Li, Yun; Zhao, Huamin; Li, Xiaobin; Yuan, Xiangyang

doi:10.3390/s22218206

Cited by 10 publications

(13 citation statements)

References 40 publications

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“…In experiments where the performance gap is obvious, we will directly use mean Average Precision (mAP) [ 33 ] as the evaluation index, as shown in the following formula:

where TP is the number of correctly identified signal samples, FP is the number of incorrectly or unidentified signal samples, and FN is the number of incorrectly identified signal sample targets. C s is the number of categories of signal samples; M and N represent the number of IOU thresholds and IOU thresholds; P ( k ) and R ( k ) is the precision and recall rates.…”

Section: Methodsmentioning

confidence: 99%

Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network

Guo

Yao

et al. 2023

Sensors

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With the development of wireless technology, signals propagating in space are easy to mix, so blind detection of communication signals has become a very practical and challenging problem. In this paper, we propose a blind detection method for broadband signals based on a weighted bi-directional feature pyramid network (BiFPN). The method can quickly perform detection and automatic modulation identification (AMC) on time-domain aliased signals in broadband data. Firstly, the method performs a time-frequency analysis on the received signals and extracts the normalized time-frequency images and the corresponding labels by short-time Fourier transform (STFT). Secondly, we build a target detection model based on YOLOv5 for time-domain mixed signals in broadband data and learn the features of the time-frequency distribution image dataset of broadband signals, which achieves the purpose of training the model. The main improvements of the algorithm are as follows: (1) a weighted bi-directional feature pyramid network is used to achieve a simple and fast multi-scale feature fusion approach to improve the detection probability; (2) the Efficient-Intersection over Union (EIOU) loss function is introduced to achieve high accuracy signal detection in a low Signal-Noise Ratio (SNR) environment. Finally, the time-frequency images are detected by an improved deep network model to complete the blind detection of time-domain mixed signals. The simulation results show that the method can effectively detect the continuous and burst signals in the broadband communication signal data and identify their modulation types.

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“…In experiments where the performance gap is obvious, we will directly use mean Average Precision (mAP) [ 33 ] as the evaluation index, as shown in the following formula:

Section: Methodsmentioning

confidence: 99%

Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network

Guo

Yao

et al. 2023

Sensors

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“…The biggest advantage of YOLO is that it is extremely fast, which makes it a great advantage in real-time detection tasks. YOLOv5 algorithm [ 32 – 34 ] makes some improvements on the basis of YOLOv4 [ 35 ], tasks. YOLOv5 algorithm [ 32 – 34 ] makes some improvements on the basis of YOLOv4 [ 35 ], so that its speed and accuracy in target detection are greatly improved.…”

Section: Methodsmentioning

confidence: 99%

YOLO-plum: A high precision and real-time improved algorithm for plum recognition

Niu,

Lu,

Liang

et al. 2023

PLoS ONE

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Real-time, rapid, accurate, and non-destructive batch testing of fruit growth state is crucial for improving economic benefits. However, for plums, environmental variability, multi-scale, occlusion, overlapping of leaves or fruits pose significant challenges to accurate and complete labeling using mainstream algorithms like YOLOv5. In this study, we established the first artificial dataset of plums and used deep learning to improve target detection. Our improved YOLOv5 algorithm achieved more accurate and rapid batch identification of immature plums, resulting in improved quality and economic benefits. The YOLOv5-plum algorithm showed 91.65% recognition accuracy for immature plums after our algorithmic improvements. Currently, the YOLOv5-plum algorithm has demonstrated significant advantages in detecting unripe plums and can potentially be applied to other unripe fruits in the future.

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“…However, the generation of these similar feature maps causes an increase in computational effort. To facilitate the deployment of the model at the edge, the GhostNet network can be used to reconstruct the YOLOv4 backbone network with a simple linear transformation to produce the same rich feature maps, making it less computationally intensive and the network more lightweight [ 25 ]. The GhostNet network mainly has a Ghost Module, which is combined to build the model architecture.…”

Section: Improved Yolov4 Insulator Target Detection and Defect Identi...mentioning

confidence: 99%

A Lightweight Algorithm for Insulator Target Detection and Defect Identification

Han

Zhao

et al. 2023

Sensors

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The accuracy of insulators and their defect identification by UAVs (unmanned aerial vehicles) in transmission-line inspection needs to be further improved, and the model size of the detection algorithm is significantly reduced to make it more suitable for edge-end deployment. In this paper, the algorithm uses a lightweight GhostNet module to reconstruct the backbone feature extraction network of the YOLOv4 model and employs depthwise separable convolution in the feature fusion layer. The model is lighter on the premise of ensuring the effect of image information extraction. Meanwhile, the ECA-Net channel attention mechanism is embedded into the feature extraction layer and PANet (Path Aggregation Network) to improve the recognition accuracy of the model for small targets. The experimental results show that the size of the improved model is reduced from 244 MB to 42 MB, which is only 17.3% of the original model. At the same time, the mAp of the improved model is 0.77% higher than that of the original model, reaching 95.4%. Moreover, the mAP compared with YOLOv5-s and YOLOX-s, respectively, is improved by 1.98% and 1.29%. Finally, the improved model is deployed into Jetson Xavier NX and run at a speed of 8.8 FPS, which is 4.3 FPS faster than the original model.

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Foxtail Millet Ear Detection Method Based on Attention Mechanism and Improved YOLOv5

Cited by 10 publications

References 40 publications

Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network

Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network

YOLO-plum: A high precision and real-time improved algorithm for plum recognition

A Lightweight Algorithm for Insulator Target Detection and Defect Identification

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