Opium Poppy Detection Using Deep Learning

Liu, Xiangyu; Tian, Yongchao; Yuan, Chao; Zhang, Feifei; Yang, Guang

doi:10.3390/rs10121886

Cited by 18 publications

(13 citation statements)

References 37 publications

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“…DCN was used to detect multiple targets including plane, ship, vehicle and oil tank in remote sensing images by Ren et al [33]. Liu et al [34] used SSD to detect opium poppy in ZiYuan3 remote sensing images. Unsupervised-restricted CNN [35] was modified from DSSD for detecting different kinds of targets from the data by Geoeye and Quickbird sensors.…”

Section: Deep Learning Based Object Detection In Rsismentioning

confidence: 99%

“…For more detailed introduction to how each model works, please refer to the respective citations. [24] ResNet-101 two-stage [33] SSD [26] ResNet-101 one-stage [34] DSSD [28] ResNet-101 one-stage [35] YOLOv3 [30] Darknet-53 one-stage [36] RetinaNet [31] ResNet-101 one-stage [37] 3.1. Deep Learning Models for Comparative Study…”

Section: Deep Learning Based Fossil-fuel Power Plant Monitoringmentioning

confidence: 99%

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Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study

Zhang

Deng

2019

Remote Sensing

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The frequent hazy weather with air pollution in North China has aroused wide attention in the past few years. One of the most important pollution resource is the anthropogenic emission by fossil-fuel power plants. To relieve the pollution and assist urban environment monitoring, it is necessary to continuously monitor the working status of power plants. Satellite or airborne remote sensing provides high quality data for such tasks. In this paper, we design a power plant monitoring framework based on deep learning to automatically detect the power plants and determine their working status in high resolution remote sensing images (RSIs). To this end, we collected a dataset named BUAA-FFPP60 containing RSIs of over 60 fossil-fuel power plants in the Beijing-Tianjin-Hebei region in North China, which covers about 123 km 2 of an urban area. We compared eight state-of-the-art deep learning models and comprehensively analyzed their performance on accuracy, speed, and hardware cost. Experimental results illustrate that our deep learning based framework can effectively detect the fossil-fuel power plants and determine their working status with mean average precision up to 0.8273, showing good potential for urban environment monitoring.

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Section: Deep Learning Based Object Detection In Rsismentioning

confidence: 99%

Section: Deep Learning Based Fossil-fuel Power Plant Monitoringmentioning

confidence: 99%

Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study

Zhang

Deng

2019

Remote Sensing

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“…Liu et al did not need to extract features manually, using two ZiYuan3 (ZY3) remote sensing images to create six training data sets with different band combinations and sliding window sizes, and training single-shot multi-box detectors (SSD) models, respectively. This proposed method obtains a higher rate of identification of poppy plots [9]. Tang et al proposed a multi-view YOLO object detection method, model structure and working method, which improved the ability to detect small objects.…”

Section: Introductionmentioning

confidence: 99%

Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection

Huang

et al. 2019

Applied Sciences

117

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Target detection of electronic components on PCB (Printed circuit board) based on vision is the core technology for 3C (Computer, Communication and Consumer Electronics) manufacturing companies to achieve quality control and intelligent assembly of robots. However, the number of electronic components on PCB is large, and the shape is different. At present, the accuracy of the algorithm for detecting all electronic components is not high. This paper proposes an improved algorithm based on YOLO (you only look once) V3 (Version 3), which uses a real PCB picture and a virtual PCB picture with synthesized data as a joint training dataset, which greatly increases the recognizability of training electronic components and provides the greatest possibility for data enhancement. After analyzing the feature distribution of the five dimensionality-reduced output layers of Darknet-53 and the size distribution of the detection target, it is proposed to adjust the original three YOLO output layers to four YOLO output layers and generate 12 anchor boxes for electronic component detection. The experimental results show that the mean average precision (mAP) of the improved YOLO V3 algorithm can achieve 93.07%.

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“…Taylor et al [1], along with the U.S. government, used satellite remote sensing to detect poppy plots in Afghanistan for several years. Liu et al [2] used ZY-3 satellite imagery to detect poppy plots in Phongsali Province, Laos, using the single-shot detector (SSD)-based object detection method. Jia et al [3] studied the spectral characteristics of three different poppy growth stages, showing that the best period for distinguishing poppy from coexisting crops was during flowering.…”

Section: Introductionmentioning

confidence: 99%

Improved UAV Opium Poppy Detection Using an Updated YOLOv3 Model

Zhou

Tian

Yuan

et al. 2019

Sensors

Self Cite

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Rapid detection of illicit opium poppy plants using UAV (unmanned aerial vehicle) imagery has become an important means to prevent and combat crimes related to drug cultivation. However, current methods rely on time-consuming visual image interpretation. Here, the You Only Look Once version 3 (YOLOv3) network structure was used to assess the influence that different backbone networks have on the average precision and detection speed of an UAV-derived dataset of poppy imagery, with MobileNetv2 (MN) selected as the most suitable backbone network. A Spatial Pyramid Pooling (SPP) unit was introduced and Generalized Intersection over Union (GIoU) was used to calculate the coordinate loss. The resulting SPP-GIoU-YOLOv3-MN model improved the average precision by 1.62% (from 94.75% to 96.37%) without decreasing speed and achieved an average precision of 96.37%, with a detection speed of 29 FPS using an RTX 2080Ti platform. The sliding window method was used for detection in complete UAV images, which took approximately 2.2 sec/image, approximately 10× faster than visual interpretation. The proposed technique significantly improved the efficiency of poppy detection in UAV images while also maintaining a high detection accuracy. The proposed method is thus suitable for the rapid detection of illicit opium poppy cultivation in residential areas and farmland where UAVs with ordinary visible light cameras can be operated at low altitudes (relative height < 200 m).

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Opium Poppy Detection Using Deep Learning

Cited by 18 publications

References 37 publications

Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study

Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study

Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection

Improved UAV Opium Poppy Detection Using an Updated YOLOv3 Model

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