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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (3) : 90-97     DOI: 10.6046/gtzyyg.2020.03.12
Informal garbage dumps detection in high resolution remote sensing images based on SU-RetinaNet
WU Tong1,2,3(), PENG Ling1,2(), HU Yuan1,2,3
1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
3. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
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The improvement of urbanization level and the imperfection of waste treatment infrastructure in China have caused the problem that informal garbage stacking becomes more and more prominent. With the development of high resolution remote sensing images, macro and efficient management of informal garbage dumps becomes possible. Most of the existing researches use visual interpretation and traditional supervised classification methods to identify informal garbage dumps from high resolution remote sensing images. Such methods are very time-consuming, and it is difficult for them to mine deep features from data, therefore the detection accuracy is limited. Therefore, the authors used convolutional neural network, proposed an informal garbage dumps detection framework SU-RetinaNet from high resolution remote sensing image based on sample updated and RetinaNet, analyzed the impact of different parameters and network structure on the model detection performance, and compared the results of using DPM, R-CNN , Faster R-CNN, RetinaNet and SU-RetinaNet 5 algorithms for the performance of informal garbage dumps detection. Experimental results show that the use of SU-RetinaNet for informal garbage dumps detection can achieve average precision of 85.92% and a detection speed of 0.097 s per image. Compared with traditional method, SU-RetinaNet greatly improves the detection efficiency of informal garbage dumps, and provides a feasible technical solution for effective monitoring and management of municipal waste.

Keywords high resolution remote sensing images      convolutional neural network      SU-RetinaNet      informal garbage dumps detection     
:  P237  
Corresponding Authors: PENG Ling     E-mail:;
Issue Date: 09 October 2020
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Tong WU,Ling PENG,Yuan HU. Informal garbage dumps detection in high resolution remote sensing images based on SU-RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(3): 90-97.
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Fig.1  Informal garbage dumps detection process based on SU-RetinaNet
Fig.2  RetinaNet network structure
Fig.3  Training data and composite samples of informal garbage dumps
Fig.4  Flow chart of the proposed method
Fig.5  Initial detection results of informal garbage dumps on test set
阶段 特征提取网络深度 α=0.25,
阶段1 ResNet50 68.26 74.47 76.88 75.71 75.03 79.38 72.62 75.98 75.63
ResNet101 72.52 72.09 74.25 70.84 72.49 78.78 70.93 77.09 75.46
ResNet152 75.28 77.48 68.85 73.33 73.84 74.80 71.65 76.22 74.98
阶段2 ResNet50 81.39 82.34 84.54 82.93 85.18 85.38 84.28 87.25 85.98
ResNet101 81.49 83.76 83.17 78.99 83.77 83.59 79.97 82.03 83.26
ResNet152 79.89 82.6 79.32 80.35 84.33 83.18 77.7 82.41 84.08
Tab.1  Comparison of AP on the validation set under different parameter configurations using RetinaNet (%)
学习率 AP/%
0.000 50 78.66
0.000 10 87.25
0.000 05 86.62
0.000 01 86.48
Tab.2  Influence of different learning rate on AP
Fig.6  Final detection results of informal garbage dumps on test set
方法 AP/% 平均检测时间/s
DPM 42.00 30.348
R-CNN 66.00 8.636
Faster R-CNN 80.70 0.108
RetinaNet 77.12 0.097
SU-RetinaNet 85.92 0.097
Tab.3  Detection performance comparison of 5 different object detection methods
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