Airport Area Detection Based on Optimized Regional Convolutional Neural Network

Step 1: Extract the area and proportion information of the ground truth of some targets in each type of target from the regional proposal network as a sample.Step 2: The information extracted from various targets is transformed into a two-dimensional European space.Step 3: Initialize 9 anchor boxes randomly (the number selection is modeled after the Faster R-CNN detection algorithm. Too much is easy to multiply the calculation amount, and too few is not easy to represent the full scale of the target) and compare the 9 anchor boxes with all of the selected samples ground truth information and calculate the difference value of each box.Step 4: The ground truth with small difference value is divided into a combination around the corresponding anchor box.Step 5: Calculate the average size of the ground truth in each combination as a new anchor box.Step 6: Repeat the above steps until the difference does not change much after each iteration, and get the best 9 anchor boxes.

Table 2. Comparison of T1 improved algorithm and original algorithm performance
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Table 2. Comparison of T1 improved algorithm and original algorithm performance
Method mAP /% Mean time /s
Faster R-CNN 67.5 0.142
Faster R-CNN+T1 70.3 0.142

Table 3. Comparison of improved algorithm and original algorithm performance of adding T2
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Table 3. Comparison of improved algorithm and original algorithm performance of adding T2
Method mAP /% Mean time /s
Faster R-CNN 67.5 0.142
Faster R-CNN+T2 68.8 0.143

Table 4. Prior judgment algorithm steps

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Table 4. Prior judgment algorithm steps

A priori decision implementation steps

Step 1: Read the classification results of the detection network from the log file (where the labels are assigned to the values 0, 1, 2, …, 6 in the order in Table 6) and the corresponding confidence levels.Step 2: If multiple types of labels are detected and the product of the label values is 0, then Step 3 is performed, otherwise the label name is directly output.Step 3: Compare the average of the detection confidence of the target with a non-zero label to the average of the target detection confidence with a label value of 0 to obtain a label with a larger average confidence value. If the target average confidence level with a label value of 0 is large, 0 is output, otherwise all other non-zero label values are output.Step 4: Read the label value in Step 3 and output the corresponding label name.

Table 5. Comparison of experimental data sets and traditional data sets

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Table 5. Comparison of experimental data sets and traditional data sets

Item	Traditional remote sensing target detection data set	Experimental target detection data set
Category	Single class	Multi-class
Scale	Medium/large	Small/medium/large scale(especially focusing on small scale targets)
Perspective	Vertical viewing angle	30°, 60°, 90°, etc. Multi-viewing angle
Background	Simple background	Focus on target detection incomplex backgrounds(especially airport backgrounds)

Table 6. Label and its corresponding target comparison table
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Table 6. Label and its corresponding target comparison table
Label airport airplane_mh airplane_z airplane_zs airplane_y bridge oiltank
Object Airport Civil aircraft Fighter Helicopter Transport Bridge Oil tank

Table 7. Algorithm steps

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Table 7. Algorithm steps

Algorithm steps

Step 1: Train the region proposal network separately, initialize the weights by the pre-trained model, and adjust the parameters in an end-to-end manner to give a proposal region.Step 2: Train the detection network separately. The region area for training comes from Step1. The weights are initialized using a pre-trained model.Step 3: Use the parameters of the Step2 detection model to initialize the regional proposal network while fixing the convolutional layer, and adjust only the regional proposal network parameters.Step 4: Use the proposal area output from Step3 as the input to the detection network, while keeping the shared convolutional layer fixed and fine-tune the remaining detection network parameters.

Table 8. Summary of each target test results
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Table 8. Summary of each target test results
Object Airport Civil aircraft Helicopter Fighter Transport Oil tank Bridge
AP /% 80.8415 84.8188 70.0974 62.1441 71.0077 73.5869 68.7273

Table 9. Airport test results
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Table 9. Airport test results

Table 10. Civil aviation aircraft test results
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Table 10. Civil aviation aircraft test results

Table 11. Target test results under multiple categories
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Table 11. Target test results under multiple categories

Table 12. Comparison of various target detection results between improved method and original algorithm
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Table 12. Comparison of various target detection results between improved method and original algorithm
Method AP /% Meantime /s
Airport Civil aircraft Helicopter Fighter Transport Oil tank Bridge
Faster R-CNN 76.66 80.56 66.82 58.62 67.56 69.02 64.85 0.142
Proposed 80.84 84.82 70.10 62.14 71.01 73.59 68.73 0.145

Table 13. Comparison of results of different detection methods
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Table 13. Comparison of results of different detection methods
Object Method AP /% Mean time /s
Ref. [19] 76.73 6.87
Civilaircraft Faster R-CNN 80.56 0.142
Proposed 84.82 0.145
Ref. [7] 72.78 20.86
Airport Faster R-CNN 76.66 0.142
Proposed 80.84 0.145

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Yongsai Han, Shiping Ma, Shuai Li, Linyuan He, Mingming Zhu. Airport Area Detection Based on Optimized Regional Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101021

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