Laser & Optoelectronics Progress, Volume. 61, Issue 10, 1028001(2024)

Remote-Sensing Image Object Detection Based on Improved YOLOv8 Algorithm

Xiuzai Zhang1,2、*, Tao Shen1, and Dai Xu1
Author Affiliations
  • 1School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China
  • 2Jiangsu Province Atmospheric Environment and Equipment Technology Collaborative Innovation Center, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China
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    A target detection algorithm based on improved YOLOv8 is proposed to address the issues of high-missed and false-detection rates, inaccurate target positioning, and inability to accurately identify target categories in remote-sensing image target detection algorithms. To improve the flexibility of the loss function of the model in gradient allocation and adapt to various object shapes and sizes, a boundary box regression loss function is designed, which combines a nonmonotonic focusing mechanism with geometric factors of the boundary box. To expand the receptive field of the model and weaken the influence of the remote-sensing image background on the detection target, a residual global attention mechanism is designed by combining global attention mechanism and residual blocks. To adapt the model to the deformation and irregular arrangement of target objects in remote-sensing images, the C2f module in the YOLOv8 model is improved by incorporating deformable convolution and deformable region-of-interest pooling layers. Experimental results show that on DOTA and RSOD datasets, mean average precision (mAP@0.5) of the improved YOLOv8 algorithm reaches 72.1% and 94.6%, which are better than other mainstream algorithms. It improves the accuracy of remote sensing image target detection and provides a new means for remote sensing image target detection.

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    Xiuzai Zhang, Tao Shen, Dai Xu. Remote-Sensing Image Object Detection Based on Improved YOLOv8 Algorithm[J]. Laser & Optoelectronics Progress, 2024, 61(10): 1028001

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Jul. 31, 2023

    Accepted: Oct. 13, 2023

    Published Online: Apr. 29, 2024

    The Author Email: Zhang Xiuzai (zxzhering@163.com)

    DOI:10.3788/LOP231803

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