Fast Opium Poppy Detection in Unmanned Aerial Vehicle (UAV) Imagery Based on Deep Neural Network
Opium poppy is a medicinal plant, and its cultivation is illegal without legal approval in China. Unmanned aerial vehicle (UAV) is an effective tool for monitoring illegal poppy cultivation. However, targets often appear occluded and confused, and it is difficult for existing detectors to accurately...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/7/9/559 |
| _version_ | 1797580532866875392 |
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| author | Zhiqi Zhang Wendi Xia Guangqi Xie Shao Xiang |
| author_facet | Zhiqi Zhang Wendi Xia Guangqi Xie Shao Xiang |
| author_sort | Zhiqi Zhang |
| collection | DOAJ |
| description | Opium poppy is a medicinal plant, and its cultivation is illegal without legal approval in China. Unmanned aerial vehicle (UAV) is an effective tool for monitoring illegal poppy cultivation. However, targets often appear occluded and confused, and it is difficult for existing detectors to accurately detect poppies. To address this problem, we propose an opium poppy detection network, YOLOHLA, for UAV remote sensing images. Specifically, we propose a new attention module that uses two branches to extract features at different scales. To enhance generalization capabilities, we introduce a learning strategy that involves iterative learning, where challenging samples are identified and the model’s representation capacity is enhanced using prior knowledge. Furthermore, we propose a lightweight model (YOLOHLA-tiny) using YOLOHLA based on structured model pruning, which can be better deployed on low-power embedded platforms. To evaluate the detection performance of the proposed method, we collect a UAV remote sensing image poppy dataset. The experimental results show that the proposed YOLOHLA model achieves better detection performance and faster execution speed than existing models. Our method achieves a mean average precision (mAP) of 88.2% and an F1 score of 85.5% for opium poppy detection. The proposed lightweight model achieves an inference speed of 172 frames per second (FPS) on embedded platforms. The experimental results showcase the practical applicability of the proposed poppy object detection method for real-time detection of poppy targets on UAV platforms. |
| first_indexed | 2024-03-10T22:52:21Z |
| format | Article |
| id | doaj.art-a35d396188d44fffa31853f1014cf179 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T22:52:21Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-a35d396188d44fffa31853f1014cf1792023-11-19T10:17:00ZengMDPI AGDrones2504-446X2023-08-017955910.3390/drones7090559Fast Opium Poppy Detection in Unmanned Aerial Vehicle (UAV) Imagery Based on Deep Neural NetworkZhiqi Zhang0Wendi Xia1Guangqi Xie2Shao Xiang3School of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, ChinaOpium poppy is a medicinal plant, and its cultivation is illegal without legal approval in China. Unmanned aerial vehicle (UAV) is an effective tool for monitoring illegal poppy cultivation. However, targets often appear occluded and confused, and it is difficult for existing detectors to accurately detect poppies. To address this problem, we propose an opium poppy detection network, YOLOHLA, for UAV remote sensing images. Specifically, we propose a new attention module that uses two branches to extract features at different scales. To enhance generalization capabilities, we introduce a learning strategy that involves iterative learning, where challenging samples are identified and the model’s representation capacity is enhanced using prior knowledge. Furthermore, we propose a lightweight model (YOLOHLA-tiny) using YOLOHLA based on structured model pruning, which can be better deployed on low-power embedded platforms. To evaluate the detection performance of the proposed method, we collect a UAV remote sensing image poppy dataset. The experimental results show that the proposed YOLOHLA model achieves better detection performance and faster execution speed than existing models. Our method achieves a mean average precision (mAP) of 88.2% and an F1 score of 85.5% for opium poppy detection. The proposed lightweight model achieves an inference speed of 172 frames per second (FPS) on embedded platforms. The experimental results showcase the practical applicability of the proposed poppy object detection method for real-time detection of poppy targets on UAV platforms.https://www.mdpi.com/2504-446X/7/9/559opium poppy detectionUAV remote sensingdeep neural networkrepetitive learningmodel pruning |
| spellingShingle | Zhiqi Zhang Wendi Xia Guangqi Xie Shao Xiang Fast Opium Poppy Detection in Unmanned Aerial Vehicle (UAV) Imagery Based on Deep Neural Network Drones opium poppy detection UAV remote sensing deep neural network repetitive learning model pruning |
| title | Fast Opium Poppy Detection in Unmanned Aerial Vehicle (UAV) Imagery Based on Deep Neural Network |
| title_full | Fast Opium Poppy Detection in Unmanned Aerial Vehicle (UAV) Imagery Based on Deep Neural Network |
| title_fullStr | Fast Opium Poppy Detection in Unmanned Aerial Vehicle (UAV) Imagery Based on Deep Neural Network |
| title_full_unstemmed | Fast Opium Poppy Detection in Unmanned Aerial Vehicle (UAV) Imagery Based on Deep Neural Network |
| title_short | Fast Opium Poppy Detection in Unmanned Aerial Vehicle (UAV) Imagery Based on Deep Neural Network |
| title_sort | fast opium poppy detection in unmanned aerial vehicle uav imagery based on deep neural network |
| topic | opium poppy detection UAV remote sensing deep neural network repetitive learning model pruning |
| url | https://www.mdpi.com/2504-446X/7/9/559 |
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