Imagery intelligence from low altitudes: chosen aspects

Walczykowski, P.; Kędzierski, Michał

doi:10.1117/12.2269318

Cited by 1 publication

(2 citation statements)

References 5 publications

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“…It was equipped with a consumer-grade digital camera and the technical details are listed in Table 2. The camera can only take three visible bands of red, green and blue (RGB), while the near-infrared band, commonly used in vegetation analysis [14], is unavailable. We tested the proposed method on a CPU-GPU heterogeneous computer.…”

Section: Equipment: Unmanned Aerial Vehicles (Uav) and Computersmentioning

confidence: 99%

“…There were some common issues that may have caused the low-quality image of the UAV, including motion blur or camera unforces. Although there are methods that can enhance the quality of the image [24]; it was essential to arrange the flight campaign under good conditions e.g., good weather, appropriate camera adjustment, flight speed, and flight altitude [14].…”

Section: Tree Detection Accuracymentioning

confidence: 99%

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Papaya Tree Detection with UAV Images Using a GPU-Accelerated Scale-Space Filtering Method

Jiang

Chen

et al. 2017

Remote Sensing

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Abstract:The use of unmanned aerial vehicles (UAV) can allow individual tree detection for forest inventories in a cost-effective way. The scale-space filtering (SSF) algorithm is commonly used and has the capability of detecting trees of different crown sizes. In this study, we made two improvements with regard to the existing method and implementations. First, we incorporated SSF with a Lab color transformation to reduce over-detection problems associated with the original luminance image. Second, we ported four of the most time-consuming processes to the graphics processing unit (GPU) to improve computational efficiency. The proposed method was implemented using PyCUDA, which enabled access to NVIDIA's compute unified device architecture (CUDA) through high-level scripting of the Python language. Our experiments were conducted using two images captured by the DJI Phantom 3 Professional and a most recent NVIDIA GPU GTX1080. The resulting accuracy was high, with an F-measure larger than 0.94. The speedup achieved by our parallel implementation was 44.77 and 28.54 for the first and second test image, respectively. For each 4000 × 3000 image, the total runtime was less than 1 s, which was sufficient for real-time performance and interactive application.

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Section: Equipment: Unmanned Aerial Vehicles (Uav) and Computersmentioning

confidence: 99%

Section: Tree Detection Accuracymentioning

confidence: 99%