Laser & Optoelectronics Progress, Volume. 58, Issue 16, 1601002(2021)
Objects Detection from High-Resolution Remote Sensing Imagery Using Training-Optimized YOLOv3 Network
Fig. 4. RSOD dataset augmentation processing. (a)Image flipping along X axis; (b)image cropping; (c) image rotation; (d) image saturation adjustment
Fig. 5. Labeling sample of dataset using Labeling software. (a) Original image; (b) labeled image (true object bounding box)
Fig. 6. Comparison of the three basic sizes of anchor boxes before and after our training optimization. (a) Anchor boxes from traditional YOLOv3 model; (b) anchor boxes from our clustered augmented dataset
Fig. 7. Comparison of the detection results between the traditional and our optimized YOLOv3 models. (a)--(c) Traditional YOLOv3 model; (d)--(f) our optimized YOLOv3 model
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Yun Yang, Longwei Li, Siyan Gao, Han Bai, Wancheng Jiang. Objects Detection from High-Resolution Remote Sensing Imagery Using Training-Optimized YOLOv3 Network[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1601002
Category: Atmospheric Optics and Oceanic Optics
Received: Sep. 25, 2020
Accepted: Dec. 14, 2020
Published Online: Aug. 19, 2021
The Author Email: Li Longwei (1049730716@qq.com)