Detection of Pine-Wilt-Disease-Affected Trees Based on Improved YOLO v7
<p>Location of the study area. (<b>a</b>) Jilin Province is represented in red color on the map of China; (<b>b</b>) The location of the Baihe Administration Station is indicated by a red circle on the map of Jilin Province; (<b>c</b>) Natural color image of the study area obtained via helicopter.</p> "> Figure 2
<p>Sample annotation schematic of the target detection model dataset. With the yellow box indicating information about the location and size of the symptomatic trees.</p> "> Figure 3
<p>Graphical representation of proposed model.</p> "> Figure 4
<p>Improved YOLO v7 model structure diagram. With different colors representing different functional modules, where dark blue is the added attention mechanism module.</p> "> Figure 5
<p>Inflation forecast diagram and comparison of optimization effect before and after. With the pink box in (<b>b</b>) presenting the detection results before inflation prediction, and the green box in (<b>c</b>) showing the detection results after inflation prediction.</p> "> Figure 6
<p>Non-maximum suppression (NMS) optimization effect comparison chart. With the green box in (<b>a</b>) presenting the detection results before NMS, and the blue box in (<b>b</b>) showing the detection results after NMS.</p> "> Figure 7
<p>Attention mechanism module: SE: Squeeze-and-Excitation, ECA: Efficient Channel Attention, CBAM: Convolutional Block Attention Module, SimAM: Simplified Attention Module.</p> "> Figure 8
<p>Performance comparison of object detection.</p> "> Figure 9
<p>YOLO v7-SE model performance demonstration.</p> "> Figure 9 Cont.
<p>YOLO v7-SE model performance demonstration.</p> "> Figure 10
<p>YOLO v7-SE training results.</p> "> Figure 11
<p>Comparison table of target detection model performance.</p> "> Figure 12
<p>Target detection results graph. With the blue box in (<b>b</b>) showing the location and size of the detection results.</p> "> Figure 13
<p>Comparison of detection results of YOLO v7, YOLO v 7-SE, and YOLO v 7-CBAM. (<b>a</b>–<b>d</b>) show the comparison of the effectiveness of the three models in detecting symptomatic trees in four different scenarios, respectively, with green representing YOLO v7, red representing YOLO v 7-SE, and blue representing YOLO v7-CBAM.</p> "> Figure 14
<p>YOLO v7-CBAM error detection schematic. With the blue box showing the error detection results of YOLO v7-CBAM.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection
2.1.1. Overview of the Study Area
2.1.2. Data Acquisition
2.1.3. Dataset Production
2.2. Methods
2.2.1. Experiment Content
2.2.2. Target Detection Model
Introduction to YOLO v7
Target Detection Optimization Strategy
2.2.3. Introduction to the Attention Mechanisms Module
2.2.4. Test Environment and Parameter Settings
2.2.5. Accuracy Inspection
3. Results
3.1. Target Detection Model Performance Analysis
3.2. Comparative Analysis of Training Accuracy of Target Detection Model
3.3. Target Detection Model Performance Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Precision | Recall | F1 | |
---|---|---|---|
YOLO v7 | 0.6739 | 0.8072 | 0.7346 |
SE | 0.6934 | 0.8179 | 0.7505 |
CBAM | 0.6688 | 0.8098 | 0.7326 |
SimAM | 0.6367 | 0.8443 | 0.7259 |
ECA | 0.6487 | 0.8047 | 0.7183 |
YOLO v7 | SE | CBAM | SimAM | ECA | |
---|---|---|---|---|---|
Manual detection of symptomatic trees | 1843 | 1843 | 1843 | 1843 | 1843 |
Model detection of symptomatic trees | 1722 | 1779 | 2031 | 1662 | 1736 |
Correct detection of symptomatic trees | 1564 | 1651 | 1739 | 1545 | 1512 |
Precision rate | 0.9082 | 0.9281 | 0.8562 | 0.9296 | 0.8710 |
Recall rate | 0.8486 | 0.8958 | 0.9435 | 0.8383 | 0.8204 |
F1 score | 0.8774 | 0.9117 | 0.8977 | 0.8816 | 0.8449 |
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Zhu, X.; Wang, R.; Shi, W.; Liu, X.; Ren, Y.; Xu, S.; Wang, X. Detection of Pine-Wilt-Disease-Affected Trees Based on Improved YOLO v7. Forests 2024, 15, 691. https://doi.org/10.3390/f15040691
Zhu X, Wang R, Shi W, Liu X, Ren Y, Xu S, Wang X. Detection of Pine-Wilt-Disease-Affected Trees Based on Improved YOLO v7. Forests. 2024; 15(4):691. https://doi.org/10.3390/f15040691
Chicago/Turabian StyleZhu, Xianhao, Ruirui Wang, Wei Shi, Xuan Liu, Yanfang Ren, Shicheng Xu, and Xiaoyan Wang. 2024. "Detection of Pine-Wilt-Disease-Affected Trees Based on Improved YOLO v7" Forests 15, no. 4: 691. https://doi.org/10.3390/f15040691