Medicinal Chrysanthemum Detection under Complex Environments Using the MC-LCNN Model
Abstract
:1. Introduction
- A lightweight MC-LCNN model was designed to achieve high-accuracy, real-time detection of medicinal chrysanthemums under complex unstructured environments.
- A series of experiments were designed to validate the superiority of MC-LCNN, including comparisons with different data enhancements, ablation experiments between various network components, and comparisons with state-of-the-art object detection models.
- The MC-LCNN model was embedded into an edge computing device with a custom pipeline design to achieve accurate real-time medicinal chrysanthemum detection.
2. Materials and Methods
2.1. Dataset
2.2. NVIDIA Jetson TX2
2.3. MC-LCNN
2.3.1. MC-ResNetv1 and MC-ResNetv2
2.3.2. Generalized Focal Loss
2.4. CPU–GPU Multithreaded Pipeline Design
2.5. Evaluation Metrics
Experimental Setup
3. Results
3.1. The Impact of Data Augmentation on the MC-LCNN
3.2. Ablation Experiments
3.3. Comparisons with State-of-the-Art Detection Methods
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Authors | Tasks | Published Year | Test Environment | Precision | Inference Speed | Test Devices |
---|---|---|---|---|---|---|
[6] | Chrysanthemum cut detection | 1996 | Ideal | / | / | Laptop |
[7] | Chrysanthemum leaf recognition | 2000 | Ideal | / | / | Laptop |
[8] | Chrysanthemum bud testing | 2014 | Ideal | 0.75 | / | Laptop |
[9] | Chrysanthemum disease detection | 2017 | Ideal | / | / | Laptop |
[10] | Chrysanthemum variety testing | 2018 | Illumination | 0.85 | 0.4 s | Laptop |
[11] | Chrysanthemum picking | 2019 | Illumination | 0.9 | 0.7 s | Laptop |
[12] | Chrysanthemum variety classification | 2019 | Ideal | 0.78 | 10 ms | Laptop |
[1] | Chrysanthemum variety classification | 2020 | Ideal | 0.96 | / | Laptop |
[13] | Chrysanthemum image recognition | 2020 | Ideal | 0.76 | 0.3 s | Laptop |
Flip | Shear | Crop | Rotation | Grayscale | Hu | Saturation | Exposure | Blur | Noise | Cutout | Mixup | Cutmix | Mosaic | AP | AP50 | AP75 | APS | APM | APL |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
√ | 70.68 | 88.59 | 75.49 | 69.22 | 75.87 | 85.89 | |||||||||||||
√ | 70.99 | 88.63 | 75.32 | 67.01 | 75.22 | 85.28 | |||||||||||||
√ | 69.03 | 87.84 | 74.01 | 66.84 | 75.34 | 85.44 | |||||||||||||
√ | 69.56 | 88.38 | 74.42 | 66.28 | 76.03 | 85.59 | |||||||||||||
√ | 68.42 | 87.84 | 73.21 | 66.14 | 75.84 | 86.41 | |||||||||||||
√ | 68.82 | 88.44 | 73.57 | 66.11 | 76.03 | 86.04 | |||||||||||||
√ | 68.49 | 88.18 | 73.36 | 65.98 | 75.62 | 86.63 | |||||||||||||
√ | 69.93 | 89.13 | 73.52 | 66.01 | 75.83 | 86.12 | |||||||||||||
√ | 70.13 | 90.69 | 73.59 | 66.02 | 75.98 | 87.35 | |||||||||||||
√ | 68.06 | 87.11 | 71.25 | 64.39 | 72.88 | 84.11 | |||||||||||||
√ | 70.33 | 91.14 | 75.44 | 67.22 | 74.89 | 87.88 | |||||||||||||
√ | 68.46 | 88.31 | 72.53 | 65.52 | 73.46 | 85.03 | |||||||||||||
√ | 68.88 | 88.67 | 72.68 | 65.33 | 73.13 | 85.67 | |||||||||||||
√ | √ | √ | 68.87 | 88.54 | 72.23 | 65.12 | 73.06 | 85.29 | |||||||||||
√ | √ | 71.62 | 92.03 | 75.09 | 67.88 | 75.38 | 88.26 | ||||||||||||
√ | √ | 70.98 | 91.82 | 74.66 | 67.65 | 75.22 | 87.61 | ||||||||||||
√ | √ | 71.44 | 92.36 | 75.93 | 68.66 | 75.87 | 87.99 | ||||||||||||
√ | √ | 71.64 | 92.22 | 76.23 | 69.03 | 76.08 | 87.92 | ||||||||||||
√ | √ | 72.22 | 93.06 | 76.46 | 69.63 | 76.42 | 88.89 | ||||||||||||
√ | √ | √ | 71.88 | 92.62 | 76.32 | 69.12 | 76.53 | 88.22 | |||||||||||
√ | √ | √ | 71.03 | 92.03 | 75.96 | 68.99 | 75.99 | 87.53 | |||||||||||
√ | √ | √ | 70.65 | 91.87 | 74.53 | 67.63 | 75.68 | 87.04 | |||||||||||
√ | √ | √ | √ | 70.11 | 90.58 | 73.89 | 66.37 | 74.81 | 86.34 |
Method | FPS | AP | AP50 | AP75 | APS | APM | APL |
---|---|---|---|---|---|---|---|
Ours + CBM × 5 | 80.29 | 72.44 | 93.31 | 77.02 | 70.23 | 77.39 | 88.93 |
Ours + CBM × 4 | 89.83 | 72.63 | 93.33 | 77.86 | 70.99 | 77.54 | 89.25 |
Ours + CBM × 3 | 96.11 | 72.56 | 93.23 | 77.33 | 70.56 | 77.28 | 89.21 |
Ours + CBM × 2 | 101.26 | 72.34 | 93.08 | 77.02 | 70.12 | 77.25 | 88.91 |
Ours − SPP | 101.29 | 67.85 | 86.25 | 69.82 | 62.63 | 69.91 | 84.36 |
Ours − EMA | 106.69 | 70.33 | 90.94 | 73.83 | 64.45 | 74.12 | 87.11 |
Ours − DropBlock | 106.88 | 69.58 | 89.66 | 73.25 | 64.22 | 73.66 | 86.82 |
Ours (ResNet101) | 64.66 | 64.12 | 85.14 | 66.89 | 58.33 | 67.01 | 82.34 |
Ours (ResNet50) | 73.45 | 62.06 | 82.64 | 65.57 | 57.46 | 65.63 | 80.84 |
Ours (RetNeXt-101) | 92.21 | 69.36 | 88.08 | 74.12 | 65.89 | 74.33 | 85.33 |
Ours (ResNet50-vd-dcn) | 80.58 | 68.54 | 87.61 | 74.88 | 67.82 | 74.93 | 85.26 |
Ours (ResNet101-vd-dcn) | 67.99 | 68.38 | 89.96 | 74.58 | 67.66 | 74.61 | 86.01 |
Ours (EfficientB6) | 61.58 | 68.35 | 88.31 | 71.29 | 67.41 | 71.38 | 85.49 |
Ours (EfficientB5) | 67.33 | 67.68 | 87.55 | 69.84 | 66.84 | 69.85 | 85.12 |
Ours (EfficientB4) | 70.44 | 67.39 | 87.08 | 68.46 | 66.19 | 68.63 | 84.87 |
Ours (EfficientB3) | 78.09 | 66.67 | 86.42 | 70.41 | 67.88 | 70.52 | 84.58 |
Ours (EfficientB2) | 83.28 | 66.33 | 85.27 | 69.16 | 65.44 | 69.46 | 84.33 |
Ours (EfficientB1) | 85.33 | 65.64 | 83.26 | 67.33 | 62.06 | 67.42 | 82.89 |
Ours (EfficientB0) | 96.63 | 63.59 | 80.83 | 68.99 | 64.83 | 69.58 | 78.45 |
Ours (VGG16) | 76.13 | 63.87 | 81.65 | 66.89 | 61.26 | 70.34 | 78.05 |
Ours (MobileNet v1) | 83.54 | 62.66 | 79.99 | 72.67 | 66.02 | 72.93 | 76.85 |
Ours (MobileNet v2) | 79.56 | 64.48 | 82.11 | 73.43 | 66.24 | 73.67 | 80.99 |
Ours (ShuffleNet v1) | 85.84 | 65.12 | 84.12 | 69.91 | 61.41 | 70.28 | 82.24 |
Ours (ShuffleNet v2) | 76.27 | 66.69 | 87.28 | 70.57 | 62.66 | 70.88 | 84.44 |
Ours (DenseNet) | 81.02 | 67.34 | 88.54 | 69.66 | 62.16 | 69.99 | 84.83 |
Ours (DarkNet53) | 84.82 | 67.98 | 89.67 | 70.18 | 64.53 | 70.22 | 85.06 |
Ours (CSPDarknet53) | 98.21 | 68.11 | 89.82 | 72.89 | 65.98 | 72.88 | 85.54 |
Ours (CSPDenseNet) | 91.46 | 68.14 | 90.22 | 74.33 | 67.38 | 74.56 | 86.22 |
Ours (CSPRetNeXt) | 93.11 | 68.88 | 90.93 | 73.26 | 66.34 | 73.28 | 86.53 |
Ours (RetinaNet) | 62.63 | 64.09 | 84.08 | 66.28 | 60.11 | 66.54 | 81.31 |
Ours (Modified CSP v5) | 90.23 | 69.23 | 90.82 | 73.11 | 67.23 | 73.25 | 86.83 |
Ours | 109.28 | 72.22 | 93.06 | 76.46 | 69.63 | 76.42 | 88.89 |
Method | Backbone | Size | FPS | AP | AP50 | AP75 | APS | APM | APL |
---|---|---|---|---|---|---|---|---|---|
RetinaNet [19] | ResNet101 | 800 × 800 | 15.63 | 48.33 | 70.23 | 51.24 | 41.22 | 51.33 | 67.03 |
RetinaNet | ResNet50 | 800 × 800 | 18.82 | 51.61 | 76.44 | 55.09 | 44.21 | 55.43 | 69.14 |
RetinaNet | ResNet101 | 500 × 500 | 24.58 | 60.83 | 81.29 | 62.84 | 51.29 | 62.11 | 75.49 |
RetinaNet | ResNet50 | 500 × 500 | 30.99 | 63.69 | 82.99 | 64.44 | 53.09 | 64.13 | 76.58 |
EfficientDetD6 [20] | EfficientB6 | 1280 × 1280 | 10.26 | 64.13 | 85.21 | 66.45 | 56.33 | 65.91 | 77.27 |
EfficientDetD5 | EfficientB5 | 1280 × 1280 | 23.58 | 63.09 | 84.66 | 66.31 | 55.94 | 66.35 | 78.21 |
EfficientDetD4 | EfficientB4 | 1024 × 1024 | 38.61 | 62.99 | 84.33 | 65.11 | 55.31 | 65.36 | 78.01 |
EfficientDetD3 | EfficientB3 | 896 × 896 | 50.83 | 60.86 | 83.16 | 64.46 | 54.86 | 64.39 | 77.92 |
EfficientDetD2 | EfficientB2 | 768 × 768 | 68.99 | 59.54 | 82.84 | 64.08 | 54.11 | 64.12 | 77.87 |
EfficientDetD1 | EfficientB1 | 640 × 640 | 80.11 | 56.44 | 79.41 | 58.66 | 49.66 | 58.49 | 72.28 |
EfficientDetD0 | EfficientB0 | 512 × 512 | 88.29 | 53.28 | 77.96 | 55.86 | 47.26 | 55.89 | 70.21 |
M2Det [21] | VGG16 | 800 × 800 | 19.22 | 55.23 | 81.22 | 57.69 | 48.54 | 57.58 | 71.55 |
M2Det | ResNet101 | 320 × 320 | 30.54 | 52.33 | 77.38 | 56.54 | 48.44 | 56.36 | 70.83 |
M2Det | VGG16 | 512 × 512 | 33.56 | 50.19 | 74.94 | 54.46 | 46.21 | 54.32 | 69.91 |
M2Det | VGG16 | 300 × 300 | 45.44 | 49.68 | 71.86 | 51.33 | 44.37 | 52.68 | 68.58 |
YOLOv3 [22] | DarkNet53 | 608 × 608 | 45.31 | 64.65 | 86.85 | 67.23 | 58.57 | 67.66 | 74.83 |
YOLOv3(SPP) | DarkNet53 | 608 × 608 | 46.39 | 64.05 | 85.13 | 66.88 | 56.88 | 66.43 | 74.22 |
YOLOv3 | DarkNet53 | 416 × 416 | 58.62 | 61.18 | 80.08 | 63.18 | 55.01 | 63.54 | 72.84 |
YOLOv3 | DarkNet53 | 320 × 320 | 62.59 | 58.41 | 77.34 | 61.34 | 54.67 | 61.67 | 71.11 |
PFPNet (R) [23] | VGG16 | 512 × 512 | 43.11 | 52.22 | 73.59 | 56.24 | 50.88 | 56.68 | 68.42 |
PFPNet (R) | VGG16 | 320 × 320 | 52.09 | 51.35 | 72.63 | 55.12 | 48.89 | 55.37 | 67.95 |
PFPNet (s) | VGG16 | 300 × 300 | 53.64 | 55.53 | 74.33 | 59.81 | 53.22 | 60.44 | 72.67 |
RFBNetE | VGG16 | 512 × 512 | 36.99 | 60.25 | 80.03 | 62.58 | 54.27 | 62.89 | 75.21 |
RFBNet [24] | VGG16 | 512 × 512 | 52.02 | 58.11 | 76.13 | 61.06 | 53.85 | 61.46 | 75.03 |
RFBNet | VGG16 | 512 × 512 | 60.16 | 63.96 | 84.85 | 65.48 | 58.68 | 65.66 | 81.84 |
RefineDet [25] | VGG16 | 512 × 512 | 42.13 | 59.83 | 79.66 | 63.56 | 57.53 | 63.69 | 76.53 |
RefineDet | VGG16 | 448 × 448 | 58.61 | 57.51 | 78.09 | 61.11 | 56.91 | 61.41 | 75.54 |
YOLOv4 [20] | CSPDarknet53 | 608 × 608 | 49.58 | 66.99 | 88.23 | 69.64 | 60.85 | 69.98 | 86.88 |
YOLOv4 | CSPDarknet53 | 512 × 512 | 69.42 | 66.38 | 87.98 | 68.99 | 60.44 | 69.33 | 85.34 |
YOLOv4 | CSPDarknet53 | 300 × 300 | 83.28 | 63.24 | 83.43 | 66.48 | 59.68 | 66.51 | 80.28 |
YOLOv5s | CSPDenseNet | 416 × 416 | 84.11 | 65.14 | 84.33 | 68.22 | 61.24 | 68.32 | 81.11 |
YOLOv5l | CSPDenseNet | 416 × 416 | 67.03 | 66.35 | 86.26 | 69.31 | 61.37 | 69.41 | 81.33 |
YOLOv5m | CSPDenseNet | 416 × 416 | 51.22 | 67.58 | 86.67 | 69.89 | 61.99 | 70.22 | 83.59 |
YOLOv5x | CSPDenseNet | 416 × 416 | 30.68 | 68.93 | 88.64 | 72.66 | 63.12 | 72.68 | 84.44 |
PP-YOLO [26] | ResNet50-vd-dcn | 320 × 320 | 106.85 | 66.64 | 85.26 | 68.15 | 60.85 | 68.17 | 81.23 |
PP-YOLO | ResNet50-vd-dcn | 416 × 416 | 93.25 | 67.06 | 86.88 | 68.67 | 60.99 | 68.61 | 82.03 |
PP-YOLO | ResNet50-vd-dcn | 512 × 512 | 80.01 | 68.32 | 87.29 | 69.58 | 61.45 | 69.62 | 83.22 |
PP-YOLO | ResNet50-vd-dcn | 608 × 608 | 64.26 | 69.11 | 88.02 | 70.18 | 62.33 | 70.54 | 84.31 |
PP-YOLOv2 [27] | ResNet50-vd-dcn | 320 × 320 | 110.54 | 67.89 | 85.98 | 68.28 | 62.02 | 68.47 | 82.06 |
PP-YOLOv2 | ResNet50-vd-dcn | 416 × 416 | 103.88 | 67.95 | 86.13 | 68.88 | 62.55 | 70.46 | 83.11 |
PP-YOLOv2 | ResNet50-vd-dcn | 512 × 512 | 89.04 | 68.36 | 86.85 | 69.33 | 62.84 | 69.67 | 83.89 |
PP-YOLOv2 | ResNet50-vd-dcn | 608 × 608 | 81.67 | 68.88 | 87.26 | 70.06 | 63.04 | 70.33 | 84.48 |
PP-YOLOv2 | ResNet50-vd-dcn | 640 × 640 | 63.38 | 69.45 | 88.64 | 71.23 | 64.24 | 71.61 | 85.15 |
PP-YOLOv2 | ResNet101-vd-dcn | 512 × 512 | 48.98 | 69.48 | 89.22 | 71.99 | 64.53 | 72.32 | 86.67 |
PP-YOLOv2 | ResNet101-vd-dcn | 640 × 640 | 41.34 | 69.66 | 89.59 | 72.83 | 65.11 | 72.88 | 86.88 |
YOLOF [28] | RetinaNet | 512 × 512 | 102.84 | 65.53 | 86.52 | 69.03 | 62.15 | 69.11 | 83.12 |
YOLOF-R101 | ResNet-101 | 512 × 512 | 89.28 | 65.91 | 86.58 | 69.44 | 62.41 | 69.45 | 83.48 |
YOLOF-X101 | RetNeXt-101 | 512 × 512 | 68.09 | 67.56 | 88.34 | 70.95 | 62.95 | 71.06 | 85.66 |
YOLOF-X101+ | RetNeXt-101 | 512 × 512 | 53.69 | 67.94 | 88.82 | 71.38 | 63.11 | 71.44 | 85.83 |
YOLOF-X101++ | RetNeXt-101 | 512 × 512 | 36.06 | 68.25 | 89.03 | 72.63 | 64.23 | 72.61 | 86.22 |
YOLOX-DarkNet53 | Darknet-53 | 640 × 640 | 81.61 | 66.89 | 87.41 | 71.12 | 63.28 | 71.29 | 86.13 |
YOLOX-M [29] | Modified CSP v5 | 640 × 640 | 65.48 | 67.83 | 88.36 | 71.53 | 63.56 | 71.58 | 86.27 |
YOLOX-L | Modified CSP v5 | 640 × 640 | 53.54 | 69.44 | 89.14 | 73.24 | 64.93 | 73.38 | 86.35 |
YOLOX-X | Modified CSP v5 | 640 × 640 | 46.22 | 69.86 | 89.63 | 73.39 | 65.22 | 73.54 | 86.86 |
Ours | MC-ResNet | 416 × 416 | 109.28 | 72.22 | 93.06 | 76.46 | 69.63 | 76.42 | 88.89 |
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Qi, C.; Chang, J.; Zhang, J.; Zuo, Y.; Ben, Z.; Chen, K. Medicinal Chrysanthemum Detection under Complex Environments Using the MC-LCNN Model. Plants 2022, 11, 838. https://doi.org/10.3390/plants11070838
Qi C, Chang J, Zhang J, Zuo Y, Ben Z, Chen K. Medicinal Chrysanthemum Detection under Complex Environments Using the MC-LCNN Model. Plants. 2022; 11(7):838. https://doi.org/10.3390/plants11070838
Chicago/Turabian StyleQi, Chao, Jiangxue Chang, Jiayu Zhang, Yi Zuo, Zongyou Ben, and Kunjie Chen. 2022. "Medicinal Chrysanthemum Detection under Complex Environments Using the MC-LCNN Model" Plants 11, no. 7: 838. https://doi.org/10.3390/plants11070838