# Using Channel and Network Layer Pruning Based on Deep Learning for Real-Time Detection of Ginger Images

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## Abstract

**:**

^{−1}. The results showed that the proposed method was feasible for real-time and accurate detection of ginger images, providing a solid foundation for automatic and accurate ginger seeding.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Image Acquisition System

#### 2.2. Overall Technical Route

#### 2.3. Network Structure for Ginger Recognition Based on YOLO v3

#### 2.4. Sparse Training Based on Channel Importance Evaluation

^{−6}is to prevent the denominator from being 0.

_{i}| is the absolute value of the γ-value for the i

^{th}channel. The objective function of sparse training is as follows:

_{θ}is the output value of the forward propagation; l(·)denotes the training loss of the target; ||γ||

_{1}is the L1-normalization of the γ-values; α is a constant coefficient used to tune the optimization objective function.

#### 2.5. Channels and Network Layers Pruning Algorithms

^{th}percentile of all γ-values in the list. This indicates that when the γ-value of a channel is less than $\widehat{\gamma}$, it will be pruned. Thus, γ-value are obtained according to different channel pruning ratios, which enables the pruning of redundant channels. Furthermore, the largest γ-value in each channel is ranked, and its minimum value is used as the upper limit for γ-values pruning to avoid pruning all channels, which ensures the integrity of the network. If γ-values of the pruned channels are equal to 0, the performance of the pruned model is similar to the sparse training model that almost equals the basic training model.

_{1}after the Leaky-ReLU activation function. The above description showed that the channel still has some influence on the subsequent modules. This study performs the following operations to compensate for the computational bias caused by β-values. (1) When the subsequent module is CBL, E[x] of the BN layer is subtracted by β

_{1}. (2) When the subsequent is a Conv layer, the β

_{1}is added to the bias of the Conv layer. (3) When the subsequent is a Shortcut or up-sample layer, the β

_{1}is passed until it meets the CBL or a Conv layer, and operation (1) or (2) is performed.

^{th}layer to the deep L

^{th}layer are as follows:

_{l}and x

_{l+}

_{1}are the input and output of the l

^{th}residual block, respectively; and h(x

_{l})=x

_{l}denotes the identity mapping; and L denotes the number of residual blocks; and F(·) is the residual function that represents the learned residual information; and W

_{i}is the trainable weight of the residual function.

_{l+}

_{1}= x

_{l}from Equation (4), which means that this residual block has not learned helpful information, then pruning it will not affect the network performance. As a result, the residual block with a smaller mean of γ-values will be pruned.

#### 2.6. Experimental Devices and Model Testing

^{−3}, and the batch size was 16. The training strategy was set as follows: In order to obtain nine suitably sized clustering boxes before network training, the width and height of the labeled boxes in the dataset are clustered by K-means, which adopts an IoU-based distance measure with the objective function of minimizing the distance between the labeled boxes and the clustering centers, and ultimately, 9 clustering boxes are obtained, and the sizes are (24, 25), (33, 44), (41, 57), (49, 75), (212, 175), (280, 195), (236, 248), (308, 246), and (352, 342), which were applied to initialize the anchor boxes in the ginger recognition network. Besides, this study introduces the Distance Intersection over Union [38] bounding box regression loss function to improve the regression effect of the predicted box.

^{−6}to 1 × 10

^{−2}, and then adopts the conventional learning rate adjustment strategy to decay it to 1 × 10

^{−3}and 1 × 10

^{−4}at the stage of 0.7 and 0.9 of the total training epochs, respectively. This method allows the learning rate to gradually increase from small to large so that the model can reach a better initialization state. Furthermore, this study uses the training set to train single-stage object detection networks to validate the model performance, such as YOLO v3 with different backbone networks and YOLO v4, respectively. Then, the performance of the various detection networks is evaluated using ginger images from the test set to validate the advantages of the proposed pruning algorithm.

#### 2.7. Performance Evaluation Metrics

## 3. Results

#### 3.1. Parameter Selection for Sparse Training

^{−4}, 1 × 10

^{−3}, 5 × 10

^{−3}, respectively, to maintain high performance and high sparsity of the sparse model. As shown in Figure 6, when α was 1 × 10

^{−3}after sparse training of 500 epochs, the ginger recognition network had a faster convergence speed, a better convergence effect.

#### 3.2. Parameter Selection for Pruning Process

#### 3.3. Analysis of mAP Curves during the Training of a Ginger Recognition Mode

#### 3.4. Parameter Selection for Sparse Training

#### 3.5. Comparison of Different Object Detection Algorithms

^{−1}for a single image. In addition, the pruned model size was 12.2 MB larger than YOLO v3 with ShuffleNet-v2 as the backbone network, but the mAP was 2.4% higher. Compared with the YOLO v3 with MobileNet-v3, Ghost-Net and Darknet-19 as the backbone networks, and the YOLO v4, the pruned model outperformed them in terms of both mAP and model size. The above results showed that the mAP of the pruned YOLO v3 model was 98.0%, including the AP of 97.4% for ginger shoots and 98.6% for ginger seeds. Meanwhile, the pruned model was tested using the enhanced test set B, and its mAP and F1-score reached 97.2% and 93.8%, respectively, indicating that the pruned model had high noise immunity and robustness. With the acceleration of the Tensor RT inference optimizer, the mAP of the pruned model on the Jetson Nano device remained essentially unchanged, and the detection speed could reach 20 frames·s

^{−1}, which could perform the ginger detection task well.

## 4. Discussion

#### 4.1. Analyzing Pre-Trained Network and the Shape of the Bounding Box

#### 4.2. Analyzing False Recognition Results

#### 4.3. Analysis of Practical Work Requirements

^{2}·h

^{−1}, the rate of ginger shoots facing the same direction is more than 90%, and two rows can be sown at the same time. The formula for seeder efficiency is as follows:

^{−1}, and we can know that the pruned model can meet the working requirements of the seeder through the above analysis.

## 5. Conclusions

- Firstly, the ginger dataset is established, including image acquisition, data enhancement, and image annotation. Next, transfer learning and learning rate warm-up strategies are adopted to identify ginger shoots and seeds accurately. The experimental results reveal that the mAP and F1-score reach 98.1% and 95.4%, respectively, providing a reliable model compression basis.
- This study prunes the ginger recognition model’s redundant channels and network layers to reduce model parameters and inference time. First, the γ-values in the batch normalization layer are used to evaluate the channel importance, and the unimportant channels are pruned to accomplish the channel pruning. Then, the
**γ**-values of the CBL module before the Shortcut layer are taken to evaluate the significance of the residual blocks, and the insignificant residual blocks are pruned to realize the pruning of the network layer. - After channel and network layer pruning, the mAP and model size of the ginger recognition model reach 98.0% and 32.7 MB, respectively, which are reduced by 0.1% and 86.03% compared with the pre-pruning model. With the acceleration of the Tensor RT inference optimizer, the detection speed of a single 416 × 416 pixels ginger image on the Jetson Nano device can reach 20 frames·s
^{−1}. This study provides technical support for the future implementation of grasping ginger and adjusting the ginger shoot direction using end-effector devices. - The detection of ginger images is only the first step in automated ginger seeding. In the future, the use of detection results for ginger grasping and ginger shoot orientation adjustment will be a hot research topic. Furthermore, our proposed pruning algorithm is not only beneficial for ginger detection, but it can also be applied to other crop detection tasks where computational resources are limited. Future work will focus on more efficient pruning methods and the acquisition of more ginger images under real-world operating conditions to further improve the accuracy of ginger detection.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Ginger growing process. (

**a**) ginger, (

**b**) ginger after sprouting, (

**c**) artificial ginger planting, (

**d**) mechanical ginger planting.

**Figure 7.**Histogram statistics of scaling factors in all BN layers. (

**a**) γ-values for each epoch after base training, (

**b**) γ-values for each epoch after sparse training, (

**c**) γ-values for each network layer after base training, (

**d**) γ-values for each network layer after sparse training.

**Figure 10.**The mAP curve during the acquisition of ginger recognition model. (

**a**) the distribution of γ-values after basic training, (

**b**) the distribution of γ-values after sparse training, (

**c**) the distribution of γ-values after model pruning, (

**d**) the distribution of γ-values after model fine-tuning.

**Figure 11.**Visualization of convolutional neural networks. (

**a**) feature map of the first convolution layer, (

**b**) feature map of the final YOLO layer.

YOLO v3 | Number of Network Layers | Type | Input Size |
---|---|---|---|

Darknet-53 without FC layer | 0 | CBL | 416 × 416 × 3 |

1~4 | Res | 416 × 416 × 32 | |

5~11 | 2Res | 208 × 208 × 64 | |

12~36 | 8Res | 104 × 104 × 128 | |

37~61 | 8Res | 52 × 52 × 256 | |

62~74 | 4Res | 26 × 26 × 512 | |

Feature fusion layer and output layer | 75~80 | 6CBL | 13 × 13 × 521 |

81~82 | Conv + YOLO | 13 × 13 × 1024 | |

83 | Route | 13 × 13 × 521 | |

84 | CBL | 13 × 13 × 521 | |

85 | Up-sample | 13 × 13 × 256 | |

86 | Route | 26 × 26 × (256 + 512) | |

87~92 | 6CBL | 26 × 26 × 768 | |

93~94 | Conv + YOLO | 26 × 26 × 512 | |

95 | Route | 26 × 26 × 256 | |

96 | CBL | 26 × 26 × 256 | |

97 | Up-sample | 13 × 13 × 128 | |

98 | Route | 52 × 52 × (128 + 256) | |

99~104 | 6CBL | 52 × 52 × 384 | |

105~106 | Conv + YOLO | 52 × 52 × 128 |

Algorithms | Backbone | P/% | R/% | mAP/% | F1-Score/% | Model Size/MB | Detection Speed/ Frame·s^{−1} |
---|---|---|---|---|---|---|---|

YOLO v3 | ShuffleNetv2 | 83.3 | 98.0 | 95.9 | 89.4 | 20.5 | 176 |

MobileNetv3 | 85.1 | 97.6 | 96.9 | 90.4 | 95.4 | 200 | |

Ghost-Net | 85.1 | 97.7 | 97.0 | 90.5 | 94.1 | 83 | |

Darknet-19 | 88.8 | 96.3 | 97.3 | 92.3 | 69.4 | 231 | |

Darknet-53 | 93.7 | 97.3 | 98.1 | 95.4 | 234 | 100 | |

YOLO v4 | CSP-Darknet | 87.9 | 97.6 | 97.2 | 92.2 | 256.2 | 74 |

Our model (test set A) | 90.8 | 98.2 | 98.0 | 94.2 | 32.7 | 185 |

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## Share and Cite

**MDPI and ACS Style**

Fang, L.; Wu, Y.; Li, Y.; Guo, H.; Zhang, H.; Wang, X.; Xi, R.; Hou, J.
Using Channel and Network Layer Pruning Based on Deep Learning for Real-Time Detection of Ginger Images. *Agriculture* **2021**, *11*, 1190.
https://doi.org/10.3390/agriculture11121190

**AMA Style**

Fang L, Wu Y, Li Y, Guo H, Zhang H, Wang X, Xi R, Hou J.
Using Channel and Network Layer Pruning Based on Deep Learning for Real-Time Detection of Ginger Images. *Agriculture*. 2021; 11(12):1190.
https://doi.org/10.3390/agriculture11121190

**Chicago/Turabian Style**

Fang, Lifa, Yanqiang Wu, Yuhua Li, Hongen Guo, Hua Zhang, Xiaoyu Wang, Rui Xi, and Jialin Hou.
2021. "Using Channel and Network Layer Pruning Based on Deep Learning for Real-Time Detection of Ginger Images" *Agriculture* 11, no. 12: 1190.
https://doi.org/10.3390/agriculture11121190