A Tree Crown Segmentation Approach for Unmanned Aerial Vehicle Remote Sensing Images on Field Programmable Gate Array (FPGA) Neural Network Accelerator

Tree crown detection of high-resolution UAV forest remote sensing images using computer technology has been widely performed in the last ten years. In forest resource inventory management based on remote sensing data, crown detection is the most important and essential part. Deep learning technology...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 25; H. 9; S. 2729
Hauptverfasser: Ma, Jiayi, Yan, Lingxiao, Chen, Baozhe, Zhang, Li
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 25.04.2025
MDPI
Schlagworte:
Accuracy
Algorithms
Automation
Classification
Data processing
Deep learning
Digital integrated circuits
Drone aircraft
Field programmable gate arrays
neural network accelerator
Neural networks
Remote sensing
Satellites
Semantics
SoC design
tree crown segmentation
China
Pennsylvania
SoC design
neural network accelerator
tree crown segmentation
ISSN:1424-8220, 1424-8220
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Zusammenfassung:Tree crown detection of high-resolution UAV forest remote sensing images using computer technology has been widely performed in the last ten years. In forest resource inventory management based on remote sensing data, crown detection is the most important and essential part. Deep learning technology has achieved good results in tree crown segmentation and species classification, but relying on high-performance computing platforms, edge calculation, and real-time processing cannot be realized. In this thesis, the UAV images of coniferous Pinus tabuliformis and broad-leaved Salix matsudana collected by Jingyue Ecological Forest Farm in Changping District, Beijing, are used as datasets, and a lightweight neural network U-Net-Light based on U-Net and VGG16 is designed and trained. At the same time, the IP core and SoC architecture of the neural network accelerator are designed and implemented on the Xilinx ZYNQ 7100 SoC platform. The results show that U-Net-light only uses 1.56 MB parameters to classify and segment the crown images of double tree species, and the accuracy rate reaches 85%. The designed SoC architecture and accelerator IP core achieved 31 times the speedup of the ZYNQ hard core, and 1.3 times the speedup compared with the high-end CPU (Intel CoreTM i9-10900K). The hardware resource overhead is less than 20% of the total deployment platform, and the total on-chip power consumption is 2.127 W. Shorter prediction time and higher energy consumption ratio prove the effectiveness and rationality of architecture design and IP development. This work departs from conventional canopy segmentation methods that rely heavily on ground-based high-performance computing. Instead, it proposes a lightweight neural network model deployed on FPGA for real-time inference on unmanned aerial vehicles (UAVs), thereby significantly lowering both latency and system resource consumption. The proposed approach demonstrates a certain degree of innovation and provides meaningful references for the automation and intelligent development of forest resource monitoring and precision agriculture.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25092729